diff --git a/.gitignore b/.gitignore index df881da..a957afd 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,17 @@ -.idea/inspectionProfiles/ -.idea/modules.xml -Spyctres/__pycache__/ +# Python bytecode +__pycache__/ +*.py[cod] + +# Packaging / build artifacts +*.egg-info/ +build/ +dist/ + +# IDE files +.idea/ + +# Notebooks +.ipynb_checkpoints/ + +# Local development scripts +scripts/phoenix_backend_compare.py diff --git a/README.md b/README.md index cb5c256..5f6e8ed 100644 --- a/README.md +++ b/README.md @@ -1,42 +1,117 @@ # Spyctres -Spyctres is a tool for analysing stellar spectra. It can compare a measured spectrum with publicly-available -libraries of spectral templates in order to find the closest match. +Spyctres is a Python package for stellar spectral fitting and spectral typing from reduced spectra. It can compare a measured spectrum with publicly available spectral-template libraries to find the closest match. Developer: Etienne Bachelet -Please note that Spyctres is still under development. +Spyctres is still under active development. It includes core fitting utilities, instrument I/O helpers, plotting tools, and example workflows. Recent additions include PHOENIX template-based fitting and a clearer separation between generic fitting code, workflow recipes, examples, and smoke tests. + +## Features + +- spectral fitting utilities in `Spyctres/Spyctres.py` +- generic spectrum containers and reader dispatch in `Spyctres/io.py` +- PHOENIX template support in `Spyctres/phoenix.py` +- PHOENIX forward modelling in `Spyctres/phoenix_forward.py` +- fitting helpers in `Spyctres/fitting.py` +- workflow recipes in `Spyctres/recipes.py` +- plotting helpers in `Spyctres/plotting.py` ## Installation -For the time being, the best way to install Spyctres is to clone its [Github repository](https://github.com/ebachelet/Spyctres), -and run the setup.py. It is recommended that you create and activate a virtual environment before proceeding with this -installation. - -```commandline -venv> git clone https://github.com/ebachelet/Spyctres.git -venv> cd Spyctres/ -venv> python setpy.py install + +Spyctres is currently intended for local editable installs during development. Creating and activating a virtual environment first is recommended. + +Spyctres requires Python 3.12 or later. + +```bash +git clone https://github.com/ebachelet/Spyctres.git +cd Spyctres +pip install -e . +``` + +Some legacy workflows use `pysynphot` and its successor package `stsynphot`: + +```bash +pip install pysynphot stsynphot ``` -Spyctres makes use of ```pysynphot``` and its successor package ```stsynphot``` as dependencies. -These can both be installed via pip: +Those workflows also require the stellar template libraries linked from the [pysynphot installation documentation](https://pysynphot.readthedocs.io/en/latest/index.html#pysynphot-installation-setup). After downloading and unpacking them, set `PYSYN_CDBS` to their local root directory: + +```bash +export PYSYN_CDBS=/path/to/cdbs +``` + +PHOENIX workflows require additional scientific Python dependencies and a local PHOENIX template directory. + +The PHOENIX templates may be downloaded from the Goettingen Spectral Library: + +- PHOENIX archive: `https://phoenix.astro.physik.uni-goettingen.de/` +- PHOENIX v2 HiResFITS directory: `https://phoenix.astro.physik.uni-goettingen.de/data/v2.0/HiResFITS/PHOENIX-ACES-AGSS-COND-2011/` +- PHOENIX v2 wavelength file: `https://phoenix.astro.physik.uni-goettingen.de/data/v2.0/HiResFITS/WAVE_PHOENIX-ACES-AGSS-COND-2011.fits` + +The wavelength file must be placed in the root directory of the PHOENIX v2 models. + +## PHOENIX template path + +The local PHOENIX path is resolved in this order: -```commandline -venv> pip install pysynphot -venv> pip install stsynphot +1. explicit command-line value +2. environment variable `SPYCTRES_PHOENIX_DIR` +3. config file `~/.config/spyctres/config.toml` + +Example config: + +```toml +[paths] +phoenix_dir = "/path/to/PHOENIXv2" ``` -Lastly, you will need to download the stellar spectrum library used as templates in Spyctres fitting -process. Tarballs of these libraries are linked from the [```pysynphot``` ReadTheDocs page](https://pysynphot.readthedocs.io/en/latest/index.html#pysynphot-installation-setup) - in the second table. +## Quick start + +Useful entry points in the repository include: + +- `quick_example.py` for the legacy fitting workflow +- `examples/full_spectrum_classification.ipynb` for PHOENIX classification +- smoke tests under `scripts/` -Download and unpack these files into a local directory. In order to tell Spyctres where to find this library, -the path to it should be declared as an environment variable in any script or shell where the software is -run, e.g. +To open the PHOENIX example notebook: -```python -os.environ['PYSYN_CDBS'] = '/my/path/cdbs/' +```bash +jupyter lab examples/full_spectrum_classification.ipynb ``` -## Running Spyctres -The ```quick_example.py``` script in this repository provides a worked demonstration of the fitting process. \ No newline at end of file +## Supported readers + +Current reader coverage includes: + +- X-SHOOTER 1D products +- PEPSI `.dxt.nor` +- FLOYDS ASCII/CSV exports +- Gemini/GMOS ASCII exports + +Readers return a generic `SpectrumSegment` object so that fitting code can remain instrument-agnostic. + +## Project structure + +Spyctres is organized around four layers: + +- generic fitting core +- workflow recipes +- user-facing examples +- developer smoke tests + +Notable files: + +- `Spyctres/recipes.py` +- `examples/full_spectrum_classification.ipynb` +- `scripts/xshooter_fit_smoketest.py` + +## Current limitations + +PHOENIX support should still be treated as alpha. + +In particular: + +- the example notebook is a first-pass classification workflow, not a final precision analysis +- some workflows still require user judgment for wavelength windows, masking, resolving power, and continuum treatment +- instrument-specific metadata quality varies across input formats +- packaging and documentation are still minimal diff --git a/Spyctres.egg-info/PKG-INFO b/Spyctres.egg-info/PKG-INFO index 427e258..339d176 100644 --- a/Spyctres.egg-info/PKG-INFO +++ b/Spyctres.egg-info/PKG-INFO @@ -13,7 +13,8 @@ Classifier: Intended Audience :: Developers Classifier: Topic :: Software Development :: Build Tools Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3) Classifier: Programming Language :: Python :: 3 -Requires-Python: >=3.6,<4 +Classifier: Programming Language :: Python :: 3.12 +Requires-Python: >=3.12,<4 License-File: LICENSE Requires-Dist: scipy Requires-Dist: numpy diff --git a/Spyctres/Spyctres_old.py b/Spyctres/Spyctres_old.py new file mode 100755 index 0000000..1588f80 --- /dev/null +++ b/Spyctres/Spyctres_old.py @@ -0,0 +1,1102 @@ +import numpy as np +import matplotlib.pyplot as plt +from astropy.io import fits +from scipy import interpolate +import pysynphot as PS +import astropy.units as u +import astropy.constants as constantes +import astropy.modeling.physical_models as apm +import os +import speclite.filters +import speclite +import scipy.interpolate as si +from astropy.table import QTable +import pkg_resources + + +from astropy.table import QTable +from astropy.coordinates import SkyCoord, solar_system, EarthLocation, ICRS +from astropy.coordinates import UnitSphericalRepresentation, CartesianRepresentation + +import stsynphot +from synphot import units, SourceSpectrum +from synphot.models import BlackBodyNorm1D + + + +VEGA = SourceSpectrum.from_vega() + +UAS_TO_RAD = 180*3600*10**6/np.pi + +ISOCHRONES_HEADER = ['Fe', 'logAge', 'logMass', 'logL', 'logTe', 'logg', 'mbolmag', + 'Umag', 'Bmag', 'Vmag', 'Rmag', 'Imag', 'Jmag', 'Hmag', 'Kmag', + 'umag', 'gmag', 'rmag', 'imag', 'zmag', 'Gmag', 'G_BPmag', + 'G_RPmag', 'F062mag', 'F087mag', 'F106mag', 'F129mag', 'F158mag', + 'F184mag', 'F146mag', 'F213mag'] + + +class BlackBody(object): + + def __init__(self,temperature=5778): + + self.temperature = temperature + #self.bb = apm.BlackBody(temperature=self.temperature*u.K) + + def __call__(self,Lambda): + + lamb = Lambda.value + + c1 = 1.1910429723971885e+34 #Angstrom to meter^-5 + c2 = 0.014387768775039337/(lamb*self.temperature)*10**10 + c3 = lamb**4*(np.exp(c2)-1) + bb = c1/c3 + #breakpoint() + return bb*15.815130864774007#*Lambda.value#photlam, 10**-7*np.pi/(1.98644746*10**-8/Lambda.value) + + +def MCMC_photometric_distances(mcmc_chains,isochrones,filters,step = 1): + + + Av = mcmc_chains[:,:,1].ravel() + Teff = mcmc_chains[:,:,3].ravel() + Fe = mcmc_chains[:,:,4].ravel() + logg = mcmc_chains[:,:,5].ravel() + + absorptions = [np.sum(filt.response*Wang_absorption_law(1,filt.wavelength / 10000)) / np.sum(filt.response) for filt in filters[:,0]] + distances_filters = [] + mags_abs = [] + iso_index = [] + + for j in range(len(Av[::step])): + + dist_iso = (Teff[j]-isochrones['logTe'])**2+(logg[j]-isochrones['logg'])**2+(Fe[j]-isochrones['Fe'])**2 + index_iso = dist_iso.argmin() + + distances = [] + mags = [] + indexes = [] + + for ind,fil in enumerate(filters): + + mag_obs = np.random.normal(filters[ind][2],filters[ind][3]) + mag_abs = isochrones[index_iso][fil[1]] + + dist_modulus = mag_obs-mag_abs-Av[j]*absorptions[ind] + + dist = 10**((dist_modulus+5)/5) + + distances.append(dist/1000) + + mags.append(mag_abs) + + indexes.append(index_iso) + + mags_abs.append(mags) + distances_filters.append(distances) + iso_index.append(indexes) + + distances_filters = np.array(distances_filters) + mags_abs = np.array(mags_abs) + iso_index = np.array(iso_index).astype(int) + + return np.c_[[distances_filters,mags_abs,iso_index]] + + + + + +def load_isochrones(): + + resource_path = '/'.join(('data', 'Bressan_Isochrones.dat')) + template = pkg_resources.resource_filename('Spyctres', resource_path) + + ISO = np.loadtxt(template, dtype=str)[1:].astype(float) + + ISO[:, 2] = np.log10(ISO[:, 2]) + + ISO = QTable(ISO, names=ISOCHRONES_HEADER) + + return ISO + +def plot_MCMC_chains_in_HR(mcmc_chains,isochrones): + + #mask_age = isochrones['logAge']>=9\ + + for age in np.unique(isochrones['logAge'].value)[::3]: + + mask = (isochrones['logAge'].value==age) & (np.abs(isochrones['Fe']-np.median(mcmc_chains[:,:,4]))<0.1) + + plt.scatter(isochrones['logTe'].value[mask],isochrones['logg'].value[mask],label='log(Age)='+str(age)) + + + + #plt.scatter(mcmc_chains[:,:,3],mcmc_chains[:,:,5],c='k',s=1,label='MCMC chains') + plt.errorbar(np.median(mcmc_chains[:,:,3]),np.median(mcmc_chains[:,:,5]),xerr=np.std(mcmc_chains[:,:,3]),yerr=np.std(mcmc_chains[:,:,5]),fmt='.k',label='MCMC chains') + plt.legend() + plt.gca().invert_yaxis() + plt.gca().invert_xaxis() + plt.xlabel(r'$\log_{10}(T_{eff})$') + plt.ylabel(r'$log g$') + + plt.show() + + +def velocity_correction(spectrum,velocity): + + new_lambda = spectrum[:,0]*(1+velocity/constantes.c.value*1000) + + interpol = interpolate.interp1d(spectrum[:,0],spectrum[:,1],fill_value='extrapolate') + + new_spectrum = interpol(new_lambda) + + final_spectrum = np.c_[spectrum[:,0],new_spectrum] + + return final_spectrum + + + +def Barycentric_velocity(time, skycoord, location=None): + """Barycentric velocity correction. + + Uses the ephemeris set with ``astropy.coordinates.solar_system_ephemeris.set`` for corrections. + For more information see `~astropy.coordinates.solar_system_ephemeris`. + + Parameters + ---------- + time : `~astropy.time.Time` + The time of observation. + skycoord: `~astropy.coordinates.SkyCoord` + The sky location to calculate the correction for. + location: `~astropy.coordinates.EarthLocation`, optional + The location of the observatory to calculate the correction for. + If no location is given, the ``location`` attribute of the Time + object is used + + + Credit https://gist.github.com/StuartLittlefair/5aaf476c5d7b52d20aa9544cfaa936a1 + + + Returns + ------- + vel_corr : `~astropy.units.Quantity` + The velocity correction to convert to Barycentric velocities. Should be added to the original + velocity. + """ + +# if location is None: +# if time.location is None: +# raise ValueError('An EarthLocation needs to be set or passed ' +# 'in to calculate bary- or heliocentric ' +# 'corrections') +# location = time.location +# + # ensure sky location is ICRS compatible + if not skycoord.is_transformable_to(ICRS()): + raise ValueError("Given skycoord is not transformable to the ICRS") + + ep, ev = solar_system.get_body_barycentric_posvel('earth', time) # ICRS position and velocity of Earth's geocenter + #op, ov = location.get_gcrs_posvel(t) # GCRS position and velocity of observatory + # ICRS and GCRS are axes-aligned. Can add the velocities + #velocity = ev + ov # relies on PR5434 being merged + + velocity = ev + # get unit ICRS vector in direction of SkyCoord + sc_cartesian = skycoord.icrs.represent_as(UnitSphericalRepresentation).represent_as(CartesianRepresentation) + return sc_cartesian.dot(velocity).to(u.km/u.s) # similarly requires PR5434 + + + +def get_element_lines(wavelength_range = [2000,10000], require_elements=['H','HE','HG','CA','FE','MG','NA','O'],intensity_threshold=50): + + elements_lines_path = os.path.dirname(__file__)+'/data/Reader_Corliss_Lines.fits' #http://cdsarc.u-strasbg.fr/viz-bin/cat/VI/16 + lines = fits.open(elements_lines_path) + waves = lines[1].data['wavel'] + + # Air-Vacuum correction https://www.astro.uu.se/valdwiki/Air-to-vacuum%20conversion + + mask = waves.astype(float)>2000 + + s = 10**4/waves[mask] + n = 1 + 0.00008336624212083 + 0.02408926869968 / (130.1065924522 - s**2) + 0.0001599740894897 / (38.92568793293 - s**2) + waves[mask] *= n + + intensities = lines[1].data['INT'] + elements = lines[1].data['Element'] + ions = lines[1].data['Spectrum'] + + good_lines = np.c_[waves,elements,ions,intensities] + + general_mask = (intensities>intensity_threshold)&(waves>wavelength_range[0])&(waves0.1: + # return np.inf + + return 0.5*chichi + + +def fit_spectra_with_constant_star_chichi(params,star_model,spectras=[],telluric_lines_mask=None): + + log_D_s,log_Mass, Av, v_radial = params[:4] + Teff,Fe,logg = star_model[1] + + try: + model_spectrum = star_model[0] + except: + return np.inf + + radius = 10**((log_Mass-logg+4.4374)/2) + theta_s = radius/10**log_D_s*4.65066 + + normalisation = (theta_s/UAS_TO_RAD)**2 + + try: + + rescale_flux_parameters = [params[4+i] for i in range(len(spectras))] + + except: + + rescale_flux_parameters = None + + try: + + rescale_errors_parameters = [params[4+len(spectras)+i] for i in range(len(spectras))] + + except: + + rescale_errors_parameters = None + + + chichi = 0 + + for ind,spectrum in enumerate(spectras.keys()): + + data = spectras[spectrum]['spectrum'] + magnification = spectras[spectrum]['magnification'] + SED = spectras[spectrum]['SED'] + + wave = data[:,0] + + + model_flux = np.array(model_spectrum(wave*u.AA)*1.98644746*10**-8/wave) + + speed_correction = spectras[spectrum]['barycentric_velocity'].value + shifted_flux = velocity_correction(np.c_[wave,model_flux],speed_correction+v_radial) + #sbreakpoint() + #shifted_flux= np.c_[wave,model_flux] + absorption = 10**(Wang_absorption_law(Av,np.array(wave)/10000)/2.5) + #absorption = 1 + shifted_flux[:,1] *= normalisation/absorption*magnification + + shifted_flux_norm = np.copy(shifted_flux) + + if rescale_flux_parameters is not None: + + rescale_flux = 10**rescale_flux_parameters[ind] + shifted_flux_norm[:,1] /= rescale_flux + + if telluric_lines_mask is not None: + + mask = telluric_lines_mask(data[:,0]).astype(bool) + + else: + + mask = [False]*len(data) + + mask_errors = (data[:,2] != 0) & np.isfinite(data[:,2]) + + mask_final = ~mask & mask_errors + #breakpoint() + if rescale_errors_parameters is not None: + + rescale_errors = 10**rescale_errors_parameters[ind] + errors = data[:,2]*rescale_errors + else: + errors = data[:,2] + residuals = (data[mask_final,1]-shifted_flux_norm[mask_final,1])**2/errors[mask_final]**2+2*np.log(errors[mask_final])+np.log(2*np.pi) + + chichi += np.sum(residuals) + #chichi=0 + #breakpoint() + for ind_sed,line_sed in enumerate(SED): + + filt,ab_mag,err_ab_mag = line_sed + + wave = filt._wavelength + absorption = 10**(Wang_absorption_law(Av,np.array(wave)/10000)/2.5) + model_flux = np.array(model_spectrum(wave*u.AA)*1.98644746*10**-8/wave) + shifted_flux = velocity_correction(np.c_[wave,model_flux],speed_correction+v_radial) + shifted_flux[:,1] *= normalisation/absorption*magnification + predicted_mag_ab = filt.get_ab_magnitude(shifted_flux[:,1],wave) + + #flux_obs = 10**((27.4-ab_mag)/2.5) + #flux_pred = 10**((27.4-predicted_mag_ab)/2.5) + + chichi += (ab_mag-predicted_mag_ab)**2/err_ab_mag**2 + #chichi += (flux_obs-flux_pred)**2/(flux_obs*err_ab_mag)**2 + #breakpoint() + #if np.abs(ab_mag-predicted_mag_ab)>0.1: + # return np.inf + + return 0.5*chichi + +def model_spectra(params,spectras=[],catalog='k93models'): + + theta_s, Av, v_radial, log10_Teff, abundance,logg = params[:6] + Teff = 10**log10_Teff + + try: + model_spectrum = stsynphot.grid_to_spec(catalog, Teff,abundance,logg) + except: + return np.inf + + normalisation = (10**theta_s/UAS_TO_RAD)**2 + spectra = [] + + + for ind,spectrum in enumerate(spectras.keys()): + + data = spectras[spectrum]['spectrum'] + magnification = spectras[spectrum]['magnification'] + wave = data[:,0] + + + model_flux = np.array(model_spectrum(wave*u.AA)*1.99*10**-8/data[:,0]) + + speed_correction = spectras[spectrum]['barycentric_velocity'].value + shifted_flux = velocity_correction(np.c_[wave,model_flux],speed_correction+v_radial) + + absorption = 10**(Wang_absorption_law(Av,np.array(wave)/10000)/2.5) + shifted_flux[:,1] *= normalisation/absorption*magnification + + + spectra.append(shifted_flux) + + return spectra + +def star_model_new(parameters,wave,catalog='k93models'): + + + theta_s, Av,Teff,abundance,logg = parameters + + try: + model_spectrum = star_spectrum_new(Teff,abundance,logg,catalog=catalog) + except: + return np.inf + + normalisation = (10**theta_s/UAS_TO_RAD)**2 + + abso = 10**(Wang_absorption_law(Av,np.array(wave)/10000)/2.5) + flux = np.array(model_spectrum(wave*u.AA)*1.98644746*10**-8/wave) + spectrum = flux*normalisation/abso + absolute_spectrum = flux*normalisation + + model = np.c_[np.array(wave),np.array(spectrum),[1]*len(spectrum)] + absolute_model = np.c_[np.array(wave),np.array(absolute_spectrum),[1]*len(spectrum)] + + return model,absolute_model + + +def star_model(parameters,catalog='k93models'): + + + theta_s, Av,Teff,abundance,logg = parameters + + try: + starspectrum = star_spectrum(Teff,abundance,logg,catalog=catalog) + except: + return None + + normalisation = (10**theta_s/UAS_TO_RAD)**2 + + wave = starspectrum.wave * u.Angstrom + abso = 10**(Wang_absorption_law(Av,np.array(wave)/10000)/2.5) + spectrum = starspectrum.flux*normalisation* (u.erg / u.cm**2 / u.s / u.Angstrom)/abso + + model = np.c_[np.array(wave),np.array(spectrum),[1]*len(spectrum)] + + return model + +def sed_chichi(spectrum,sed): + + chichi = 0 + #breakpoint() + for obs in sed: + + filt = obs[0] + mag_ab = obs[1] + emag_ab = obs[2] + + predicted_mag_ab = filt.get_ab_magnitude(spectrum[:,1],spectrum[:,0]) + #breakpoint() + chichi += (mag_ab-predicted_mag_ab)**2/emag_ab**2 + + return chichi + + +def sed_chichi_new(spectrum,sed,magnification=1): + + chichi = 0 + #breakpoint() + for obs in sed: + + filt = obs[0] + mag_ab = obs[1] + emag_ab = obs[2] + + flux = np.array(spectrum(filt._wavelength*u.AA)*1.99*10**-8/filt._wavelength) + predicted_mag_ab = filt.get_ab_magnitude(flux,filt._wavelength) + #breakpoint() + chichi += (mag_ab-predicted_mag_ab)**2/emag_ab**2 + + return chichi + + + +def source_blend_from_flux(specs,magnifications): + + v1 = 0 + v2 = 0 + A = 0 + B = 0 + C = 0 + + t1 = 0 + t2 = 0 + t3 = 0 + t4 = 0 + t5 = 0 + t6 = 0 + t7 = 0 + + + for i in range(len(specs)): + + A += magnifications[i]**2/specs[i][:,2]**2 + B += magnifications[i]/specs[i][:,2]**2 + C += 1/specs[i][:,2]**2 + v1 += specs[i][:,1]*magnifications[i]/specs[i][:,2]**2 + v2 += specs[i][:,1]*1/specs[i][:,2]**2 + + + t1 += magnifications[i]*specs[i][:,1]/specs[i][:,2]**2 + t2 += 1/specs[i][:,2]**2 + t3 += magnifications[i]/specs[i][:,2]**2 + t4 += specs[i][:,1]/specs[i][:,2]**2 + t5 += magnifications[i]**2/specs[i][:,2]**2 + t6 += 0 + t7 += magnifications[i]**2/specs[i][:,2]**2 + + aa = np.zeros((len(A),len(A))) + bb = np.zeros((len(A),len(A))) + cc = np.zeros((len(A),len(A))) + + np.fill_diagonal(aa,A) + np.fill_diagonal(bb,B) + np.fill_diagonal(cc,C) + mat = np.block([[aa,bb],[bb,cc]]) + + + fs = (t1*t2-t3*t4)/(t5*t2-t3**2) + fb = (t7*t4-t3*t1)/(t5*t2-t3**2) + sol,res, rank, s = np.linalg.lstsq(mat,np.r_[v1,v2,],rcond=-1) + + cov = np.linalg.pinv(np.dot(mat.T,mat)) + if len(res) !=0: + chi2 = np.sum((res-np.dot(mat,sol))**2)/(len(res)-len(specs[0])) + #import pdb; pdb.set_trace() + cov *= chi2 + efs = cov.diagonal()[:len(A)]**0.5 + efb = cov.diagonal()[len(A):]**0.5 + + else: + print('Not enough spectra to estimate covariance! Not reliable covariance') + chi2 = 1 + efs = 1/(magnifications[0]-magnifications[1])*(specs[0][:,2]**2+specs[1][:,2]**2)**0.5 + efb = 1/(magnifications[0]-magnifications[1])*(specs[0][:,2]**2*magnifications[1]**2+specs[1][:,2]**2*magnifications[0]**2)**0.5 + fs = sol[:len(A)] + fb = sol[len(A):] + + return fs,fb,efs,efb + +def source_blend_from_flux2(specs,magnifications): + + mat_1_spectra = np.zeros((len(specs[0]),len(specs[0]))) + mat_mag = np.copy(mat_1_spectra) + np.fill_diagonal(mat_mag,1) + mat_blend = np.copy(mat_1_spectra) + np.fill_diagonal(mat_blend,1) + + for ind,mag in enumerate(magnifications): + + weights = specs[ind][:,2] + matmag = mat_mag*mag/weights + + + try: + mat_tot = np.r_[mat_tot,np.c_[matmag,mat_blend/weights]] + obs = np.r_[obs,specs[ind][:,1]/weights] + except: + mat_tot = np.c_[matmag,mat_blend/weights] + obs = specs[ind][:,1]/weights + + + + sol,res, rank, s = np.linalg.lstsq(mat_tot,obs,rcond=-1) + cov = np.linalg.pinv(np.dot(mat_tot.T,mat_tot)) + sigmas = cov.diagonal()**0.5 + + fs = sol[:len(specs[0])] + fb = sol[len(specs[0]):] + efs = sigmas[:len(specs[0])] + efb = sigmas[len(specs[0]):] + + return fs,fb,efs,efb + + + +def source_blend_from_flux3(specs,magnifications): + + fs = [] + fb = [] + efs = [] + efb = [] + + magnifications = np.array(magnifications) + + for ind,wave in enumerate(specs[0]): + + + #p,cov = np.polyfit(magnifications,[specs[i][ind,1] for i in range(len(specs))],1,w=[1/specs[i][ind,2] for i in range(len(specs))],cov=True) + #ffs,ffb = p + + #ffs = (specs[1][ind][1]-specs[0][ind][1])/(magnifications[1]-magnifications[0]) + #ffb = specs[0][ind][1]-ffs*magnifications[0] + + #effs = 1/np.abs(magnifications[1]-magnifications[0])*np.sqrt(specs[1][ind][2]**2+specs[0][ind][2]**2) + + fluxes = np.array([specs[i][ind,1] for i in range(len(specs))]) + weights = np.array([1/specs[i][ind,2]**2 for i in range(len(specs))]) + + weighted_mag = np.sum(weights*magnifications)/np.sum(weights) + weighted_flux = np.sum(weights*fluxes)/np.sum(weights) + + flux_source = np.sum(weights*(fluxes-weighted_flux)*(magnifications-weighted_mag))/np.sum(weights*(magnifications-weighted_mag)**2) + flux_blend = weighted_flux-flux_source*weighted_mag + + eflux_source = (1/np.sum(weights*(magnifications-weighted_mag)**2))**0.5 + eflux_blend = (1/np.sum(weights)+weighted_mag**2/np.sum(weights*(magnifications-weighted_mag)**2))**0.5 + + + fs.append(flux_source) + fb.append(flux_blend) + + efs.append(eflux_source) + efb.append(eflux_blend) + #breakpoint() + return fs,fb,efs,efb + + +def star_spectrum(Teff,abundance,logg,catalog='k93models'): + + spectrum = PS.Icat(catalog,Teff,abundance,logg) + + return spectrum + +def star_spectrum_new(Teff,abundance,logg,catalog='k93models'): + + if catalog == 'BlackBody': + + spectrum = BlackBody(temperature=Teff) + + else: + + spectrum = stsynphot.grid_to_spec(catalog, Teff,abundance,logg) + + return spectrum + +def bin_absorption(data,lambda_ref): + + steps = np.diff(lambda_ref)/2 + steps = np.r_[steps,steps[-1]] + abso = [] + + for ind,lamb in enumerate(lambda_ref): + try: + #index = np.argmin(np.abs(data[:,0]-lamb)) + index_moins = np.argmin(np.abs(data[:,0]-lamb+steps[ind])) + index_plus = np.argmin(np.abs(data[:,0]-lamb-steps[ind])) + #abso.append((data[index,1])) + abso.append(np.median(data[index_moins:index_plus,1])) + # http://www.analyticalgroup.com/download/WEIGHTED_MEAN.pdf + + except: + pass + return np.c_[lambda_ref,abso] + + +def Cardelli_absorption_law(Av,Rv,lamb): + + #articles.adsabs.harvard.edu/pdf/1989ApJ...345..245C + A=[] + B=[] + for lambI in lamb: + lambi = 1./lambI + if (lambi<1.1) & (lambi>0.3): + + a = 0.574*lambi**1.61 + b = -0.527*lambi**1.61 + + if (lambi>1.1) & (lambi<3.3): + y = lambi-1.82 + a = 1+0.17699*y-0.50477*y**2-0.02427*y**3+0.72085*y**4+0.01979*y**5-0.77530*y**6+0.32999*y**7 + b = 1.41338*y+2.28305*y**2+1.07233*y**3-5.38434*y**4-0.62251*y**5+5.30260*y**6-2.09002*y**7 + + + if (lambi<0.3) | (lambi>3.3): + + a = 0 + b = 0 + A.append(a) + B.append(b) + + a_lamb = (np.array(A)+np.array(B)/Rv)*Av + + return a_lamb + +def Wang_absorption_law(Av,lamb): + #https://iopscience.iop.org/article/10.3847/1538-4357/ab1c61/pdf + alambda = np.zeros(len(lamb)) + mask = lamb<1 + Y = 1/lamb-1.82 + + alambda[mask] = Av*(1+0.7499*Y[mask]-0.1086*Y[mask]**2-0.08909*Y[mask]**3+0.02905*Y[mask]**4+0.01069*Y[mask]**5+0.001707*Y[mask]**6-0.001002*Y[mask]**7) + + alambda[~mask] = Av*0.3722*lamb[~mask]**-2.070 + mask = alambda<0 + alambda[mask] = 0 + + return alambda + + +def absorption_law_2(Av,Rv,lamb): + + a_lamb = Av*((0.55/lamb)**Rv) + + return a_lamb + +def extract_spectrum(name): + + + spec = fits.open(name) + head = spec[0].header + + + + num_pixels = head['NAXIS1'] + ref_pixel = head['CRPIX1'] + value_ref_pixel = head['CRVAL1'] + + try: + + #LCO spectra + coef_pixel_wave = head['CD1_1'] + flux = spec[0].data[0,0] +# eflux = (spec[0].data[3,0]**2+spec[0].data[2,0])**0.5 + eflux = spec[0].data[3,0] + #eflux = np.array([1.0]*len(eflux)) + except: + #XSHOOTER spectra + coef_pixel_wave = head['CDELT1'] + flux = spec[0].data + eflux = spec[1].data + + + wavelenght = value_ref_pixel+coef_pixel_wave*(np.arange(0,num_pixels)-ref_pixel) + + mask = (flux>0) & (eflux>0) + spectrum = np.c_[wavelenght,flux,eflux][mask] + mask = np.abs(spectrum[:,2]/spectrum[:,1])<0.1 + + spectrum = np.c_[spectrum[:,0],spectrum[:,1],spectrum[:,2],mask] + + + return spectrum + + +def bin_spectrum(data,lambda_ref): + #https://arxiv.org/pdf/1705.05165.pdf + #http://www.analyticalgroup.com/download/WEIGHTED_MEAN.pdf + # match https://www.astrobetter.com/blog/2013/08/12/python-tip-re-sampling-spectra-with-pysynphot/ but gives errors + steps = np.diff(lambda_ref)/2 + steps = np.r_[steps,steps[-1]] + + mask = (lambda_ref>=data[0,0]) & (lambda_ref10**-10: + + index_moins = np.argmin(np.abs(data[:,0]-lamb+steps[ind])) + index_plus = np.argmin(np.abs(data[:,0]-lamb-steps[ind])) + + + + winside = data[index_moins:index_plus+1,0] + einside = data[index_moins:index_plus+1,2] + finside = data[index_moins:index_plus+1,1] + bins = np.array([(data[i+1,0]-data[i-1,0])/2 for i in range(index_moins,index_plus+1)]) + + efficiency = np.zeros(len(winside)) + efficiency[1:-1] = 1 + + efficiency[0] = np.abs(0.5-(data[index_moins,0]-lamb+steps[ind])) + efficiency[-1] = np.abs(0.5-(data[index_plus,0]-lamb-steps[ind])) + + flux.append(np.sum(efficiency*bins*finside)/np.sum(bins*efficiency)) + cij_line = np.zeros(len(data)) + cij_line[index_moins:index_plus+1] = efficiency*bins/np.sum(bins*efficiency) + cij.append(cij_line) + + else: + + flux.append(data[index,1]) + cij_line = np.zeros(len(data)) + cij_line[index] = 1 + cij.append(cij_line) + + except: + breakpoint() + + covariance = np.array(cij) + + #eflux = np.dot(covariance,data[:,2]**2)**0.5 + final_covariance = np.dot(covariance*data[:,2],(covariance*data[:,2]).T) + eflux = final_covariance.diagonal()**0.5 + + return np.c_[lambda_ref[mask],flux,eflux],final_covariance + +def define_ROMAN_filters(): + + ROMAN_FILTERS_RESPONSE = np.loadtxt( os.path.dirname(__file__)+'/data/Roman_Filters.dat') + + ROMAN_FILTERS_RESPONSE = QTable(ROMAN_FILTERS_RESPONSE, names=['wavelength']+['F062', 'F087', 'F106', 'F129', 'F158', 'F184', 'F146', 'F213']) + + filter_names = [i for i in ROMAN_FILTERS_RESPONSE.columns.keys()][1:] + for key in filter_names: + + filt = speclite.filters.FilterResponse(wavelength = +ROMAN_FILTERS_RESPONSE['wavelength']*10**4*u.AA , response = +ROMAN_FILTERS_RESPONSE[key], meta=dict(group_name='ROMAN', band_name=key)) + + +def define_2MASS_filters(): + + + TWOMASS_filters_path = os.path.dirname(__file__)+'/data/' + #J + data = np.loadtxt( TWOMASS_filters_path+'J_2MASS_responses.dat') + MASS_J = speclite.filters.FilterResponse(wavelength = data[:,0]*u.um , response = data[:,1], meta=dict(group_name='MASS', band_name='J')) + #H + data = np.loadtxt( TWOMASS_filters_path+'H_2MASS_responses.dat') + MASS_J = speclite.filters.FilterResponse(wavelength = data[:,0]*u.um , response = data[:,1], meta=dict(group_name='MASS', band_name='H')) + #K + data = np.loadtxt( TWOMASS_filters_path+'K_2MASS_responses.dat') + MASS_J = speclite.filters.FilterResponse(wavelength = data[:,0]*u.um , response = data[:,1], meta=dict(group_name='MASS', band_name='K')) + + +def define_GAIA_filters(): + + + GAIA_filter_path = os.path.dirname(__file__)+'/data/' + #G + data = np.loadtxt( GAIA_filter_path+'G_GAIA_responses.dat') + GAIA_G = speclite.filters.FilterResponse(wavelength = data[:,0]*u.nm , response = data[:,1], meta=dict(group_name='GAIA', band_name='G')) + +def define_SDSS_prime_filters(): + + SDSS_prime_filters_path = os.path.dirname(__file__)+'/data/' + #u' + data = np.loadtxt( SDSS_prime_filters_path+'SLOAN_SDSS.uprime_filter.dat') + SDSS_prime_u = speclite.filters.FilterResponse(wavelength = data[:,0]*u.AA , response = data[:,1], meta=dict(group_name='SDSS_prime', band_name='u')) + #g' + data = np.loadtxt( SDSS_prime_filters_path+'SLOAN_SDSS.gprime_filter.dat') + SDSS_prime_g = speclite.filters.FilterResponse(wavelength = data[:,0]*u.AA , response = data[:,1], meta=dict(group_name='SDSS_prime', band_name='g')) + #r' + data = np.loadtxt( SDSS_prime_filters_path+'SLOAN_SDSS.rprime_filter.dat') + SDSS_prime_r = speclite.filters.FilterResponse(wavelength = data[:,0]*u.AA , response = data[:,1], meta=dict(group_name='SDSS_prime', band_name='r')) + #i' + data = np.loadtxt( SDSS_prime_filters_path+'SLOAN_SDSS.iprime_filter.dat') + SDSS_prime_i = speclite.filters.FilterResponse(wavelength = data[:,0]*u.AA , response = data[:,1], meta=dict(group_name='SDSS_prime', band_name='i')) + #z' + data = np.loadtxt( SDSS_prime_filters_path+'SLOAN_SDSS.zprime_filter.dat') + SDSS_prime_z = speclite.filters.FilterResponse(wavelength = data[:,0]*u.AA , response = data[:,1], meta=dict(group_name='SDSS_prime', band_name='z')) + +def mag_to_fluxdens(seds,mag_system='Vega'): + + if mag_system=='Vega': + + ab_corr = derive_AB_correction(seds[:,0]) + + else: + + ab_corr = 0 + + mag = seds[:,1].astype(float) + emag = seds[:,2].astype(float) + pivots = np.array([i.effective_wavelength.value for i in seds[:,0]]) + + mag += ab_corr + mag_to_jansky = 10**(-0.4*(mag+48.6))/10**-23 + + jansky_to_fluxdens = mag_to_jansky*1/(3.34*10**4)*1/pivots**2 + + ejansky_to_fluxdens = emag*jansky_to_fluxdens*2.5/np.log(10) + + fluxdens = np.c_[pivots.tolist(),jansky_to_fluxdens.tolist(),ejansky_to_fluxdens.tolist()] + + + return fluxdens + + +def load_telluric_lines(threshold = 0.95): + + + telluric_lines_path = os.path.dirname(__file__)+'/data/LBL_A10_s0_w050_R0300000_T.fits' #https://www.aanda.org/articles/aa/pdf/2014/08/aa23790-14.pdf + telluric_lines = fits.open(telluric_lines_path) + telluric_lines = np.c_[telluric_lines[1].data['lam']*10000,telluric_lines[1].data['trans']] + telluric_mask = telluric_lines[:,1] Path: + xdg = os.environ.get("XDG_CONFIG_HOME", "").strip() + if xdg: + p = Path(xdg).expanduser() + if p.is_absolute(): + return p + return Path.home() / ".config" + + +def default_config_path() -> Path: + return get_xdg_config_home() / "spyctres" / "config.toml" + + +def load_user_config(path: str | os.PathLike | None = None) -> dict: + cfg_path = Path(path).expanduser() if path is not None else default_config_path() + if not cfg_path.is_file(): + return {} + with open(cfg_path, "rb") as f: + data = tomllib.load(f) + return data if isinstance(data, dict) else {} + + +def get_config_value(config: dict, *keys, default=None): + cur = config + for key in keys: + if not isinstance(cur, dict) or key not in cur: + return default + cur = cur[key] + return cur + + +def resolve_setting(cli_value, env_var_name: str | None = None, config_value=None, default=None): + if cli_value is not None: + return cli_value + if env_var_name: + env_val = os.environ.get(env_var_name, None) + if env_val not in (None, ""): + return env_val + if config_value is not None: + return config_value + return default diff --git a/Spyctres/fitting.py b/Spyctres/fitting.py new file mode 100644 index 0000000..007f9d8 --- /dev/null +++ b/Spyctres/fitting.py @@ -0,0 +1,1573 @@ +# Spyctres/fitting.py +import numpy as np +from scipy.optimize import least_squares +from numpy.polynomial.legendre import legvander + +# Observed-grid PHOENIX RV resampling is implemented in waveutils.py so that +# this fitting layer does not import the legacy monolithic Spyctres.py module. +from .io import SpectrumSegment, SpectrumCollection +from .phoenix_forward import ( + infer_segments_wave_medium, + fit_bounds_from_segments, + prepare_phoenix_native_template, + build_phoenix_native_models_for_segments, + build_native_interp_wave_grid_for_segments, + convolve_to_resolution_loglam, +) +# Why multiplicative polynomial: it is a standard way to absorb low-frequency continuum differences and calibration mismatches during full-spectrum fitting. + +# RV handling: +# Spyctres.velocity_correction is a legacy helper and is left unchanged. +# PHOENIX fitting paths report rv_kms in the standard astronomical convention: +# positive RV redshifts the template/model. Observed-grid PHOENIX paths must +# call _apply_observed_grid_rv_shift(), not velocity_correction() directly. + +from .waveutils import C_KMS, resample_flux_with_velocity_shift_observed_grid + + +def _coerce_segments_input(segments): + """ + Normalize supported segment inputs to a list of SpectrumSegment objects + plus a matching positive weight vector. + + Supported inputs + ---------------- + - SpectrumSegment + - list/tuple of SpectrumSegment + - SpectrumCollection + """ + if isinstance(segments, SpectrumCollection): + seg_list = list(segments.segments) + seg_weights = np.asarray(segments.weights, dtype=float) + collection_name = segments.name + collection_meta = dict(segments.meta) + elif isinstance(segments, SpectrumSegment): + seg_list = [segments] + seg_weights = np.ones(1, dtype=float) + collection_name = None + collection_meta = {} + elif isinstance(segments, (list, tuple)): + seg_list = list(segments) + seg_weights = np.ones(len(seg_list), dtype=float) + collection_name = None + collection_meta = {} + else: + raise TypeError( + "segments must be a SpectrumSegment, SpectrumCollection, or " + "list/tuple of SpectrumSegment objects." + ) + + if len(seg_list) == 0: + raise ValueError("No segments were provided for fitting.") + + for i, seg in enumerate(seg_list): + if not isinstance(seg, SpectrumSegment): + raise TypeError( + "All segment inputs must be SpectrumSegment objects; " + "got type {0} at index {1}.".format(type(seg).__name__, i) + ) + + if seg_weights.ndim != 1 or len(seg_weights) != len(seg_list): + raise ValueError("Segment weights must be 1D and match the number of segments.") + if not np.all(np.isfinite(seg_weights)): + raise ValueError("Segment weights must be finite.") + if np.any(seg_weights <= 0): + raise ValueError("Segment weights must be > 0.") + + return seg_list, seg_weights, collection_name, collection_meta + + +def _resolve_broadening_fwhm_kms(R=None, fwhm_kms=None): + """ + Resolve the effective Gaussian FWHM in km/s. + Exactly one of R or fwhm_kms may be provided. + """ + if (R is not None) and (fwhm_kms is not None): + raise ValueError("Provide only one of R or fwhm_kms, not both.") + + if fwhm_kms is not None: + fwhm_kms = float(fwhm_kms) + if fwhm_kms <= 0: + raise ValueError("fwhm_kms must be > 0.") + return fwhm_kms + + if R is None: + return None + + R = float(R) + if R <= 0: + raise ValueError("R must be > 0.") + return C_KMS / R + + +def _resolve_segment_fwhm_kms(seg, R=None, fwhm_kms=None): + """ + Resolve the Gaussian LSF FWHM in km/s for one segment. + + Precedence: + 1. seg.meta["lsf_fwhm_kms"] + 2. seg.meta["fwhm_kms"] + 3. seg.meta["resolution_R"] + 4. global fwhm_kms + 5. global R + 6. None + """ + meta = getattr(seg, "meta", {}) or {} + + for key in ("lsf_fwhm_kms", "fwhm_kms"): + val = meta.get(key, None) + if val is None: + continue + val = float(val) + if val <= 0: + raise ValueError( + "{0} must be > 0 for segment {1}".format(key, getattr(seg, "name", None)) + ) + return val + + val = meta.get("resolution_R", None) + if val is not None: + val = float(val) + if val <= 0: + raise ValueError( + "resolution_R must be > 0 for segment {0}".format(getattr(seg, "name", None)) + ) + return C_KMS / val + + return _resolve_broadening_fwhm_kms(R=R, fwhm_kms=fwhm_kms) + + +def _gaussian_broaden_velocity(wave, flux, fwhm_kms=None): + """ + Compatibility wrapper for Gaussian velocity-space broadening. + + The canonical implementation lives in Spyctres.phoenix_forward as + convolve_to_resolution_loglam(), because instrumental broadening is part of + the forward model. This wrapper is kept temporarily for older internal calls. + """ + return convolve_to_resolution_loglam( + wave_A=wave, + flux=flux, + fwhm_kms=fwhm_kms, + ) + + +def _to_bool_mask(x, threshold=0.5): + a = np.asarray(x) + if a.dtype == bool: + return a + return a > threshold + + +def _select_region(wave, regions): + """Return boolean mask selecting points inside any (wmin,wmax) in regions.""" + if regions is None: + return np.ones_like(wave, dtype=bool) + m = np.zeros_like(wave, dtype=bool) + for (wmin, wmax) in regions: + m |= (wave >= wmin) & (wave <= wmax) + return m + + +def _exclude_region(wave, exclude_regions): + """Return boolean mask True for points NOT in any excluded interval.""" + if exclude_regions is None: + return np.ones_like(wave, dtype=bool) + m = np.ones_like(wave, dtype=bool) + for (wmin, wmax) in exclude_regions: + m &= ~((wave >= wmin) & (wave <= wmax)) + return m + + +def build_effective_fit_mask(seg, regions=None, exclude_regions=None, exclude_mask=None): + """ + Build the effective boolean fit mask for a single SpectrumSegment. + + This reproduces the point-selection logic used by fit_phoenix_full_spectrum + for a single segment, except that if seg.err is None it does not invent a + synthetic error array. In that case only finite wave/flux and the supplied + mask/region logic are applied. + """ + wave = np.asarray(seg.wave, dtype=float) + flux = np.asarray(seg.flux, dtype=float) + + m = np.asarray(seg.mask, dtype=bool) + m &= np.isfinite(wave) & np.isfinite(flux) + + if seg.err is not None: + err = np.asarray(seg.err, dtype=float) + m &= np.isfinite(err) & (err > 0) + + m &= _select_region(wave, regions) + m &= _exclude_region(wave, exclude_regions) + + if exclude_mask is not None: + m &= ~_to_bool_mask(exclude_mask(wave)) + + return m + + +def build_excluded_mask(seg, regions=None, exclude_regions=None, exclude_mask=None): + """ + Build a boolean mask of pixels explicitly excluded by region/exclude rules. + + This is intended for plotting diagnostics. It does not mark pixels excluded + only because they are NaN, have non-positive errors, or lie outside seg.mask. + """ + wave = np.asarray(seg.wave, dtype=float) + m = np.zeros_like(wave, dtype=bool) + + if regions is not None: + m |= ~_select_region(wave, regions) + + if exclude_regions is not None: + m |= ~_exclude_region(wave, exclude_regions) + + if exclude_mask is not None: + m |= _to_bool_mask(exclude_mask(wave)) + + return m + + +def _estimate_sigma(flux): + """Rough robust sigma estimate for flux if no errors are provided.""" + f = np.asarray(flux, dtype=float) + med = np.nanmedian(f) + mad = np.nanmedian(np.abs(f - med)) + sig = 1.4826 * mad + if not np.isfinite(sig) or sig <= 0: + sig = np.nanstd(f) + if not np.isfinite(sig) or sig <= 0: + sig = 1.0 + return float(sig) + + +def _build_data_vectors( + segments, + segment_weights=None, + regions=None, + exclude_regions=None, + exclude_mask=None, +): + """ + Build synchronized support-wave and fit-point data vectors. + + Returns + ------- + support_wave_all : ndarray + Concatenated full support wavelength grid across retained segments. + flux_fit_all, err_fit_all : ndarray + Concatenated flux/error vectors for fit pixels only. + support_slices : list[slice] + Per-segment slices into support_wave_all. + fit_slices : list[slice] + Per-segment slices into flux_fit_all / err_fit_all. + fit_masks : list[ndarray(bool)] + Boolean masks mapping each segment support grid to its fit pixels. + fit_weights : ndarray + Positive per-segment weights aligned with the retained segment list. + seg_meta : list[dict] + Per-segment metadata for retained segments. + """ + if segment_weights is None: + segment_weights = np.ones(len(segments), dtype=float) + else: + segment_weights = np.asarray(segment_weights, dtype=float) + if segment_weights.ndim != 1 or len(segment_weights) != len(segments): + raise ValueError("segment_weights must be 1D and match the number of segments.") + + support_wave_all = [] + flux_fit_all = [] + err_fit_all = [] + support_slices = [] + fit_slices = [] + fit_masks = [] + fit_weights = [] + seg_meta = [] + + start_support = 0 + start_fit = 0 + + for i, (seg, seg_weight) in enumerate(zip(segments, segment_weights)): + w_full = np.asarray(seg.wave, dtype=float) + f_full = np.asarray(seg.flux, dtype=float) + + support_ok = np.isfinite(w_full) & np.isfinite(f_full) + + if seg.err is None: + e_full = np.ones_like(f_full) * _estimate_sigma( + f_full[support_ok] if np.any(support_ok) else f_full + ) + err_ok = np.isfinite(e_full) & (e_full > 0) + else: + e_full = np.asarray(seg.err, dtype=float) + err_ok = np.isfinite(e_full) & (e_full > 0) + + support_ok &= err_ok + + if isinstance(regions, dict): + reg = regions.get(i, regions.get(seg.name, None)) + else: + reg = regions + + if isinstance(exclude_regions, dict): + ex = exclude_regions.get(i, exclude_regions.get(seg.name, None)) + else: + ex = exclude_regions + + fit_m = build_effective_fit_mask( + seg, + regions=reg, + exclude_regions=ex, + exclude_mask=exclude_mask, + ) + fit_m &= support_ok + + n_support = int(np.sum(support_ok)) + n_fit = int(np.sum(fit_m)) + + if n_support == 0 or n_fit == 0: + continue + + w_support = w_full[support_ok].astype(float) + f_fit = f_full[fit_m].astype(float) + e_fit = e_full[fit_m].astype(float) + + support_wave_all.append(w_support) + flux_fit_all.append(f_fit) + err_fit_all.append(e_fit) + + support_slices.append(slice(start_support, start_support + n_support)) + fit_slices.append(slice(start_fit, start_fit + n_fit)) + fit_masks.append(fit_m[support_ok]) + fit_weights.append(float(seg_weight)) + + seg_meta.append({ + "name": seg.name, + "index": int(i), + "weight": float(seg_weight), + "wave_min": float(w_support.min()), + "wave_max": float(w_support.max()), + "n_support": n_support, + "n_fit": n_fit, + }) + + start_support += n_support + start_fit += n_fit + + support_wave_all = np.concatenate(support_wave_all) if support_wave_all else np.array([], dtype=float) + flux_fit_all = np.concatenate(flux_fit_all) if flux_fit_all else np.array([], dtype=float) + err_fit_all = np.concatenate(err_fit_all) if err_fit_all else np.array([], dtype=float) + fit_weights = np.asarray(fit_weights, dtype=float) + + return ( + support_wave_all, + flux_fit_all, + err_fit_all, + support_slices, + fit_slices, + fit_masks, + fit_weights, + seg_meta, + ) + + +def _pick_subgrid(full_grid, center, half_width, n_min=3, n_max=None): + """ + Pick a small sorted subgrid around 'center' from a known full_grid. + half_width is in the same units as the grid. + """ + g = np.asarray(full_grid, dtype=float) + if g.ndim != 1 or g.size == 0: + raise ValueError("full_grid must be a non-empty 1D array.") + + lo = center - half_width + hi = center + half_width + sub = g[(g >= lo) & (g <= hi)] + + if sub.size < n_min: + # fall back to nearest points + n = int(n_min if n_max is None else min(n_min, n_max)) + idx = np.argsort(np.abs(g - center))[:max(1, n)] + return np.sort(g[idx]) + + if n_max is not None and sub.size > n_max: + idx = np.argsort(np.abs(sub - center))[:int(n_max)] + sub = sub[idx] + + return np.sort(sub) + + +def _apply_observed_grid_rv_shift(wave, model_flux, rv_kms): + """ + Apply RV to a model already sampled on the observed wavelength grid. + + PHOENIX fitter convention + ------------------------- + The PHOENIX fitting API reports rv_kms using the standard astronomical + convention: + + positive RV => redshifted template/model features + lambda_observed = lambda_rest * (1 + RV / c) + + Legacy compatibility + -------------------- + This wrapper delegates to waveutils.py rather than importing the legacy + monolithic Spyctres.py module. The legacy Spyctres.velocity_correction() + public API remains unchanged. + """ + return resample_flux_with_velocity_shift_observed_grid( + wave=wave, + flux=model_flux, + rv_kms=rv_kms, + ) + + +def _chi2_for_params( + support_wave_all, flux_all, err_all, + support_slices, fit_slices, fit_masks, segment_weights, + teff, feh, logg, rv_tot, phoenix_lib, mdeg, + decimate=1, + segment_fwhm_kms=None, +): + """ + Compute weighted chi-square with per-segment multiplicative polynomial solved linearly. + Used for RV initialization. + + The model is built on the full support wavelength grid, but chi2 is + evaluated only on the fit pixels inside each segment. + """ + model0 = phoenix_lib.evaluate(teff, feh, logg) + shifted = _apply_observed_grid_rv_shift(support_wave_all, model0, rv_tot) + chi2 = 0.0 + + if segment_fwhm_kms is None: + segment_fwhm_kms = [None] * len(support_slices) + + for support_sl, fit_sl, fit_mask, seg_weight, seg_fwhm in zip( + support_slices, fit_slices, fit_masks, segment_weights, segment_fwhm_kms + ): + w_support = support_wave_all[support_sl] + m_support = _gaussian_broaden_velocity( + w_support, + shifted[support_sl], + fwhm_kms=seg_fwhm, + ) + + w = w_support[fit_mask] + f = flux_all[fit_sl] + e = err_all[fit_sl] + m = m_support[fit_mask] + + if decimate and int(decimate) > 1: + idx = np.arange(len(w))[::int(decimate)] + w = w[idx] + f = f[idx] + e = e[idx] + m = m[idx] + + m_corr, _ = _solve_multiplicative_legendre(w, f, e, m, mdeg=mdeg) + r = (f - m_corr) / e + chi2 += float(seg_weight) * float(np.sum(r * r)) + + return chi2 + + +def _make_forward_segments(segments, support_wave_all, support_slices, fit_masks): + """ + Build support-grid SpectrumSegment objects for forward-model evaluation. + + These segments live on the cleaned support wavelength grids used internally + by fitting.py, with seg.mask marking the fit pixels on each support grid. + """ + out = [] + for seg, support_sl, fit_mask in zip(segments, support_slices, fit_masks): + w = np.asarray(support_wave_all[support_sl], dtype=float) + out.append( + SpectrumSegment( + wave=w, + flux=np.ones_like(w, dtype=float), + err=np.ones_like(w, dtype=float), + mask=np.asarray(fit_mask, dtype=bool), + meta=dict(getattr(seg, "meta", {})), + wave_medium=getattr(seg, "wave_medium", None), + wave_frame=getattr(seg, "wave_frame", None), + name=getattr(seg, "name", None), + ) + ) + return out + + +def _chi2_for_params_native_interp( + forward_segments, + flux_all, + err_all, + fit_slices, + fit_masks, + segment_weights, + teff, + feh, + logg, + rv_tot, + phoenix_lib, + model_wave_grid, + model_wave_medium, + mdeg, + decimate=1, + segment_fwhm_kms=None, + model_margin_A=200.0, +): + """ + Compute weighted chi-square for the native_interp branch. + + The PHOENIX model is interpolated in parameter space on a dense model-space + wavelength grid, then shifted, convolved, and resampled to each segment + support grid before continuum fitting. + """ + model_dense = phoenix_lib.evaluate(teff, feh, logg) + + model_list = build_phoenix_native_models_for_segments( + segments=forward_segments, + phoenix_wave_native=model_wave_grid, + template_flux_native=model_dense, + rv_kms=rv_tot, + rv_bary_kms=0.0, + segment_fwhm_kms=segment_fwhm_kms, + phoenix_wave_medium=model_wave_medium, + model_margin_A=model_margin_A, + bounds_use_fit_mask=True, + extrapolate=True, + ) + + chi2 = 0.0 + for seg, model_full, fit_sl, fit_mask, seg_weight in zip( + forward_segments, model_list, fit_slices, fit_masks, segment_weights + ): + w = np.asarray(seg.wave, dtype=float)[fit_mask] + f = flux_all[fit_sl] + e = err_all[fit_sl] + m = np.asarray(model_full, dtype=float)[fit_mask] + + if decimate and int(decimate) > 1: + idx = np.arange(len(w))[::int(decimate)] + w = w[idx] + f = f[idx] + e = e[idx] + m = m[idx] + + m_corr, _ = _solve_multiplicative_legendre(w, f, e, m, mdeg=mdeg) + r = (f - m_corr) / e + chi2 += float(seg_weight) * float(np.sum(r * r)) + + return chi2 + + +def _solve_multiplicative_legendre(wave, flux, err, model_flux, mdeg): + """ + Solve for multiplicative Legendre polynomial coefficients c such that: + flux ≈ model_flux * P(x), with P(x) = V(x) @ c + + This solves the weighted least squares problem in flux space: + minimize || (flux - model_flux*(V@c)) / err ||^2 + """ + if mdeg < 0: + raise ValueError("mdeg must be >= 0") + + good = np.isfinite(model_flux) & np.isfinite(flux) & np.isfinite(err) & (err > 0) & (model_flux != 0) + if np.sum(good) < (mdeg + 1): + return model_flux, np.r_[1.0, np.zeros(mdeg)] + + w = wave[good] + f = flux[good] + e = err[good] + m = model_flux[good] + + # Map wavelength to [-1, 1] for Legendre basis + denom = (w.max() - w.min()) + if denom == 0: + return model_flux, np.r_[1.0, np.zeros(mdeg)] + x = 2.0 * (w - w.min()) / denom - 1.0 + + V = legvander(x, mdeg) # (N, mdeg+1) + + # Weighted linear system: (m/e)*V @ c ≈ (f/e) + wgt = 1.0 / e + A = V * (m * wgt)[:, None] + b = f * wgt + + coeffs, _, _, _ = np.linalg.lstsq(A, b, rcond=None) + + # Apply polynomial to all points + denom_all = (wave.max() - wave.min()) + if denom_all == 0: + poly = np.ones_like(wave) + else: + x_all = 2.0 * (wave - wave.min()) / denom_all - 1.0 + V_all = legvander(x_all, mdeg) + poly = V_all @ coeffs + + return model_flux * poly, coeffs + + +def evaluate_legendre_continuum(wave_eval, wave_ref, coeffs): + """ + Evaluate a Legendre continuum on wave_eval using the same wavelength + normalization that was defined by wave_ref during the fit. + + Parameters + ---------- + wave_eval : array-like + Wavelength grid where the continuum should be evaluated. + wave_ref : array-like + Reference wavelength grid that defined the [-1, 1] normalization used + when fitting coeffs. + coeffs : array-like + Legendre coefficients. + + Returns + ------- + poly : ndarray + Continuum multiplicative factor on wave_eval. + """ + wave_eval = np.asarray(wave_eval, dtype=float) + wave_ref = np.asarray(wave_ref, dtype=float) + coeffs = np.asarray(coeffs, dtype=float) + + if coeffs.ndim != 1 or coeffs.size == 0: + raise ValueError("coeffs must be a non-empty 1D array.") + + if wave_ref.size == 0: + return np.ones_like(wave_eval, dtype=float) + + wmin = float(np.min(wave_ref)) + wmax = float(np.max(wave_ref)) + denom = wmax - wmin + + if (not np.isfinite(denom)) or (denom <= 0): + return np.ones_like(wave_eval, dtype=float) * float(coeffs[0]) + + x = 2.0 * (wave_eval - wmin) / denom - 1.0 + V = legvander(x, coeffs.size - 1) + return V @ coeffs + + +def reconstruct_phoenix_legendre_models_for_segments( + segments, + phoenix_lib, + fit_result, + exclude_mask=None, + mdeg=2, + rv_bary_kms=0.0, + R=None, + fwhm_kms=None, + forward_model=None, + model_margin_A=None, +): + """ + Reconstruct per-segment fitted PHOENIX model arrays on the full pixel grid + of each segment using the standard multiplicative Legendre continuum model. + + This is intended for plotting and fit diagnostics. It mirrors the poly-mode + behavior of fit_phoenix_full_spectrum, but evaluates the final continuum- + corrected model on each segment's full wavelength grid rather than only on + the fit pixels. + + Returns + ------- + model_full_list : list[ndarray] + Continuum-corrected model on the full grid of each input segment. + coeffs_list : list[ndarray] + Legendre coefficients for each segment. + used_masks : list[ndarray(bool)] + Effective fit masks actually used for each segment. + excluded_masks : list[ndarray(bool)] + Explicit exclusion masks for plotting diagnostics. + """ + segments, _segment_weights, _collection_name, _collection_meta = _coerce_segments_input(segments) + + teff = float(fit_result["teff"]) + feh = float(fit_result["feh"]) + logg = float(fit_result["logg"]) + rv_kms = float(fit_result["rv_kms"]) + + if forward_model is None: + forward_model = str(fit_result.get("forward_model", "interp_observed")) + if model_margin_A is None: + model_margin_A = float(fit_result.get("model_margin_A", 200.0)) + + used_masks = [build_effective_fit_mask(seg, exclude_mask=exclude_mask) for seg in segments] + excluded_masks = [build_excluded_mask(seg, exclude_mask=exclude_mask) for seg in segments] + segment_fwhm_kms = [ + _resolve_segment_fwhm_kms(seg, R=R, fwhm_kms=fwhm_kms) + for seg in segments + ] + model_full_list = [] + coeffs_list = [] + + if forward_model == "interp_observed": + support_lengths = [len(seg.wave) for seg in segments] + n_support_total = int(sum(support_lengths)) + + model_support_all = np.asarray(phoenix_lib.evaluate(teff, feh, logg), dtype=float) + if len(model_support_all) != n_support_total: + raise ValueError( + "Model grid length does not match total support wavelength grid: " + "{0} vs {1}".format(len(model_support_all), n_support_total) + ) + + i0 = 0 + for seg, used_mask, seg_fwhm in zip(segments, used_masks, segment_fwhm_kms): + wave_full = np.asarray(seg.wave, dtype=float) + flux_full = np.asarray(seg.flux, dtype=float) + + if seg.err is None: + sigma = _estimate_sigma(flux_full[used_mask] if np.any(used_mask) else flux_full) + err_full = np.ones_like(flux_full, dtype=float) * sigma + else: + err_full = np.asarray(seg.err, dtype=float) + + n_support = len(wave_full) + i1 = i0 + n_support + + model0_full = model_support_all[i0:i1] + shifted_full = _apply_observed_grid_rv_shift( + wave_full, + model0_full, + rv_bary_kms + rv_kms, + ) + model_broad_full = _gaussian_broaden_velocity( + wave_full, + shifted_full, + fwhm_kms=seg_fwhm, + ) + if np.any(used_mask): + w_used = wave_full[used_mask] + f_used = flux_full[used_mask] + e_used = err_full[used_mask] + m_used = model_broad_full[used_mask] + + _model_corr_used, coeffs = _solve_multiplicative_legendre( + w_used, f_used, e_used, m_used, mdeg=mdeg + ) + poly_full = evaluate_legendre_continuum(wave_full, w_used, coeffs) + model_full = np.asarray(model_broad_full, dtype=float) * poly_full + else: + coeffs = np.r_[1.0, np.zeros(int(mdeg), dtype=float)] + model_full = np.asarray(model_broad_full, dtype=float) + + model_full_list.append(model_full) + coeffs_list.append(np.asarray(coeffs, dtype=float)) + i0 = i1 + + elif forward_model == "native_interp": + model_dense = np.asarray(phoenix_lib.evaluate(teff, feh, logg), dtype=float) + + model_wave_medium = infer_segments_wave_medium( + segments, + default=getattr(phoenix_lib, "phoenix_wave_medium", "vacuum"), + ) + + model_raw_list = build_phoenix_native_models_for_segments( + segments=segments, + phoenix_wave_native=np.asarray(phoenix_lib.wave, dtype=float), + template_flux_native=model_dense, + rv_kms=rv_kms, + rv_bary_kms=rv_bary_kms, + segment_fwhm_kms=segment_fwhm_kms, + phoenix_wave_medium=model_wave_medium, + model_margin_A=model_margin_A, + bounds_use_fit_mask=True, + extrapolate=True, + ) + + for seg, used_mask, model_broad_full in zip(segments, used_masks, model_raw_list): + wave_full = np.asarray(seg.wave, dtype=float) + flux_full = np.asarray(seg.flux, dtype=float) + + if seg.err is None: + sigma = _estimate_sigma(flux_full[used_mask] if np.any(used_mask) else flux_full) + err_full = np.ones_like(flux_full, dtype=float) * sigma + else: + err_full = np.asarray(seg.err, dtype=float) + + if np.any(used_mask): + w_used = wave_full[used_mask] + f_used = flux_full[used_mask] + e_used = err_full[used_mask] + m_used = np.asarray(model_broad_full, dtype=float)[used_mask] + + _model_corr_used, coeffs = _solve_multiplicative_legendre( + w_used, f_used, e_used, m_used, mdeg=mdeg + ) + poly_full = evaluate_legendre_continuum(wave_full, w_used, coeffs) + model_full = np.asarray(model_broad_full, dtype=float) * poly_full + else: + coeffs = np.r_[1.0, np.zeros(int(mdeg), dtype=float)] + model_full = np.asarray(model_broad_full, dtype=float) + + model_full_list.append(model_full) + coeffs_list.append(np.asarray(coeffs, dtype=float)) + + else: + raise ValueError("forward_model must be 'interp_observed' or 'native_interp'.") + + return model_full_list, coeffs_list, used_masks, excluded_masks + + +def diagnose_phoenix_fixed_params( + segments, + phoenix_lib, + params, + regions=None, + exclude_regions=None, + exclude_mask=None, + mdeg=2, + rv_bary_kms=0.0, + R=None, + fwhm_kms=None, + forward_model="native_interp", + model_margin_A=200.0, +): + """ + Evaluate a PHOENIX model at fixed parameters and return per-segment + residual diagnostics before and after the multiplicative Legendre continuum. + + This function does not optimize. It is intended for debugging structured + residuals and comparing candidate parameter sets on exactly the same + data pixels, masks, broadening, wavelength grid, and continuum model. + + Important + --------- + This diagnostic assumes that phoenix_lib has already been built on the + wavelength grid required by the chosen forward_model. The normal use pattern + is therefore: + + 1. run fit_phoenix_full_spectrum(...) + 2. call diagnose_phoenix_fixed_params(...) with the same segments, + phoenix_lib, forward_model, model_margin_A, R/fwhm settings, and masks. + + Parameters + ---------- + segments : SpectrumSegment, SpectrumCollection, or sequence of SpectrumSegment + Spectrum data to diagnose. + + phoenix_lib : PhoenixLibrary + PHOENIX library whose interpolator has already been built on the + correct support grid. + + params : sequence or dict + Either (teff, feh, logg, rv_kms), or a dict containing keys + 'teff', 'feh', 'logg', and 'rv_kms'. + + Returns + ------- + result : dict + Contains per-segment wavelength, flux, error, raw model, continuum- + corrected model, residuals, continuum coefficients, and chi-square + summaries. + """ + segments, segment_weights, collection_name, collection_meta = _coerce_segments_input(segments) + + if forward_model not in ("interp_observed", "native_interp"): + raise ValueError("forward_model must be 'interp_observed' or 'native_interp'.") + + if isinstance(params, dict): + teff = float(params["teff"]) + feh = float(params["feh"]) + logg = float(params["logg"]) + if "rv_kms" in params: + rv_kms = float(params["rv_kms"]) + else: + rv_kms = float(params["rv"]) + else: + teff, feh, logg, rv_kms = map(float, params) + + ( + support_wave_all, + flux_all, + err_all, + support_slices, + fit_slices, + fit_masks, + fit_weights, + seg_meta, + ) = _build_data_vectors( + segments, + segment_weights=segment_weights, + regions=regions, + exclude_regions=exclude_regions, + exclude_mask=exclude_mask, + ) + + if support_wave_all.size == 0 or flux_all.size == 0: + raise ValueError("No data points selected for fixed-parameter diagnostic.") + + forward_segments = _make_forward_segments( + segments=segments, + support_wave_all=support_wave_all, + support_slices=support_slices, + fit_masks=fit_masks, + ) + + segment_fwhm_kms = [ + _resolve_segment_fwhm_kms(seg, R=R, fwhm_kms=fwhm_kms) + for seg in forward_segments + ] + + if forward_model == "interp_observed": + model_wave_grid = support_wave_all + + segment_media = sorted(set(str(seg.wave_medium).lower() for seg in segments)) + if len(segment_media) == 1: + model_wave_medium = segment_media[0] + else: + model_wave_medium = None + + if phoenix_lib.wave is None: + raise RuntimeError("PHOENIX interpolator is not built.") + + if (len(phoenix_lib.wave) != len(model_wave_grid)) or ( + not np.allclose(phoenix_lib.wave, model_wave_grid, rtol=0.0, atol=0.0) + ): + raise ValueError( + "PHOENIX interpolator wavelength grid does not match the " + "diagnostic support grid. Run fit_phoenix_full_spectrum first " + "with the same segments and forward_model, or rebuild the " + "interpolator on this grid." + ) + + else: + model_wave_grid, model_wave_medium = build_native_interp_wave_grid_for_segments( + segments=forward_segments, + phoenix_lib=phoenix_lib, + model_margin_A=model_margin_A, + ) + + if phoenix_lib.wave is None: + raise RuntimeError("PHOENIX interpolator is not built.") + + if (len(phoenix_lib.wave) != len(model_wave_grid)) or ( + not np.allclose(phoenix_lib.wave, model_wave_grid, rtol=0.0, atol=0.0) + ): + raise ValueError( + "PHOENIX interpolator wavelength grid does not match the " + "native diagnostic grid. Run fit_phoenix_full_spectrum first " + "with the same segments, forward_model, model_margin_A, and " + "parameter grid, or rebuild the interpolator on this grid." + ) + + model0 = np.asarray(phoenix_lib.evaluate(teff, feh, logg), dtype=float) + + if forward_model == "interp_observed": + rv_tot = float(rv_bary_kms) + float(rv_kms) + shifted = _apply_observed_grid_rv_shift(support_wave_all, model0, rv_tot) + + model_full_list = [] + for support_sl, seg_fwhm in zip(support_slices, segment_fwhm_kms): + w_support = support_wave_all[support_sl] + model_full_list.append( + _gaussian_broaden_velocity( + w_support, + shifted[support_sl], + fwhm_kms=seg_fwhm, + ) + ) + + else: + model_full_list = build_phoenix_native_models_for_segments( + segments=forward_segments, + phoenix_wave_native=model_wave_grid, + template_flux_native=model0, + rv_kms=rv_kms, + rv_bary_kms=rv_bary_kms, + segment_fwhm_kms=segment_fwhm_kms, + phoenix_wave_medium=model_wave_medium, + model_margin_A=model_margin_A, + bounds_use_fit_mask=True, + extrapolate=True, + ) + + segment_results = [] + chi2_raw_total = 0.0 + chi2_corr_total = 0.0 + n_total = 0 + + for seg, model_raw_full, fit_sl, fit_mask, seg_weight, seg_fwhm, meta in zip( + forward_segments, + model_full_list, + fit_slices, + fit_masks, + fit_weights, + segment_fwhm_kms, + seg_meta, + ): + wave_full = np.asarray(seg.wave, dtype=float) + model_raw_full = np.asarray(model_raw_full, dtype=float) + + wave_fit = wave_full[fit_mask] + model_raw = model_raw_full[fit_mask] + flux = flux_all[fit_sl] + err = err_all[fit_sl] + + model_corr, coeffs = _solve_multiplicative_legendre( + wave_fit, + flux, + err, + model_raw, + mdeg=mdeg, + ) + + resid_raw = (flux - model_raw) / err + resid_corr = (flux - model_corr) / err + + chi2_raw = float(np.sum(resid_raw * resid_raw)) + chi2_corr = float(np.sum(resid_corr * resid_corr)) + n = int(resid_corr.size) + + chi2_raw_total += float(seg_weight) * chi2_raw + chi2_corr_total += float(seg_weight) * chi2_corr + n_total += n + + segment_results.append( + { + "name": meta.get("name"), + "index": meta.get("index"), + "weight": float(seg_weight), + "wave": wave_fit.copy(), + "flux": flux.copy(), + "err": err.copy(), + "model_raw": model_raw.copy(), + "model_corr": model_corr.copy(), + "coeffs": np.asarray(coeffs, dtype=float), + "resid_raw": resid_raw.copy(), + "resid_corr": resid_corr.copy(), + "chi2_raw": chi2_raw, + "chi2_corr": chi2_corr, + "chi2_raw_weighted": float(seg_weight) * chi2_raw, + "chi2_corr_weighted": float(seg_weight) * chi2_corr, + "chi2_red_corr": chi2_corr / max(1, n - (int(mdeg) + 1)), + "n": n, + "lsf_fwhm_kms": None if seg_fwhm is None else float(seg_fwhm), + "resolution_R_effective": None if seg_fwhm is None else float(C_KMS / seg_fwhm), + "wave_min": float(np.min(wave_fit)) if n else np.nan, + "wave_max": float(np.max(wave_fit)) if n else np.nan, + "resid_corr_median": float(np.nanmedian(resid_corr)) if n else np.nan, + "resid_corr_std": float(np.nanstd(resid_corr)) if n else np.nan, + } + ) + + n_cont = len(segment_results) * (int(mdeg) + 1) + dof_effective = max(1, int(n_total) - int(n_cont)) + + return { + "params": { + "teff": teff, + "feh": feh, + "logg": logg, + "rv_kms": rv_kms, + "rv_bary_kms": float(rv_bary_kms), + }, + "forward_model": str(forward_model), + "model_margin_A": float(model_margin_A), + "mdeg": int(mdeg), + "collection_name": collection_name, + "collection_meta": collection_meta, + "segments": segment_results, + "segment_names": [s["name"] for s in segment_results], + "segment_weights": [float(w) for w in fit_weights], + "segment_lsf_fwhm_kms": [ + None if x is None else float(x) for x in segment_fwhm_kms + ], + "segment_resolution_R_effective": [ + None if x is None else float(C_KMS / x) for x in segment_fwhm_kms + ], + "chi2_raw_total": float(chi2_raw_total), + "chi2_corr_total": float(chi2_corr_total), + "n_total": int(n_total), + "n_continuum_params": int(n_cont), + "dof_effective": int(dof_effective), + "chi2_red_corr_effective": float(chi2_corr_total / dof_effective), + } + + +def default_telluric_regions_optical_angstrom(): + """ + Very small default set of strong O2 bands in the optical. + From molecfit documentation: O2 γ (0.628–0.634 µm), O2 B (0.686–0.695 µm), O2 A (0.759–0.772 µm). + """ + return [ + (6280.0, 6340.0), # O2 gamma + (6860.0, 6950.0), # O2 B + (7590.0, 7720.0), # O2 A (not in your current PEPSI red-009 range, but harmless) + ] + + +def fit_phoenix_full_spectrum( + segments, + phoenix_lib, + p0, + bounds=None, + regions=None, + exclude_regions=None, + exclude_mask=None, + mdeg=2, + rv_bary_kms=0.0, + R=None, + fwhm_kms=None, + forward_model="interp_observed", + model_margin_A=200.0, + teff_grid=None, + feh_grid=None, + logg_grid=None, + cache_path=None, + allow_missing=False, + rv_init="grid", + rv_grid_n=81, + rv_grid_decimate=5, + x_scale=None, + verbose=0, + max_nfev=200, + ): + """ + Fit PHOENIX templates to one or more SpectrumSegment objects. + + The nonlinear fit parameters are `(teff, feh, logg, rv_kms)`. At each model + evaluation, the PHOENIX spectrum is interpolated in parameter space and then + forwarded to the data using one of two wavelength-space model paths: + + - `forward_model="interp_observed"`: + interpolate directly on the observed support wavelength grid, then apply the + PHOENIX RV convention through _apply_observed_grid_rv_shift(), and broaden + there. This is a legacy/fast compatibility path. It is not the recommended + scientific path when line profiles are important. + + - `forward_model="native_interp"`: + interpolate on a dense model-space wavelength grid, then apply the + standard-sign RV shift, convolve in velocity/log-lambda space, and resample + last to each segment support grid. This is the recommended scientific path + for PHOENIX line-profile fitting. + + In both cases, the model is multiplied by a per-segment Legendre polynomial + continuum solved analytically by weighted least squares. + + Parameters + ---------- + segments : SpectrumSegment, SpectrumCollection, or sequence of SpectrumSegment + Input spectrum segments to fit. A SpectrumCollection may also carry + per-segment weights used in the joint objective. + + phoenix_lib : PhoenixLibrary + PHOENIX template library interface from `Spyctres.phoenix`, pointing to + a local PHOENIX installation. + + p0 : tuple + Initial guess `(teff, feh, logg, rv_kms)`. + + bounds : tuple, optional + Parameter bounds as + `((teff_min, feh_min, logg_min, rv_min), (teff_max, feh_max, logg_max, rv_max))`. + If None, defaults to the requested PHOENIX subgrid bounds. + + regions : None, list[tuple], or dict, optional + Inclusion regions in wavelength. May be: + - None: use all wavelengths + - list of `(wmin, wmax)` tuples applied to all segments + - dict mapping segment index or `seg.name` to a list of `(wmin, wmax)` + + exclude_regions : None, list[tuple], or dict, optional + Exclusion regions in wavelength, with the same format as `regions`. + + exclude_mask : callable, optional + Callable applied to each segment wavelength array. Points where the + returned mask is True are excluded. Non-boolean outputs are converted to + boolean using a threshold (`> 0.5`), which is useful for Spyctres + telluric masks. + + mdeg : int, optional + Degree of the multiplicative Legendre polynomial solved independently + for each segment. `mdeg=0` corresponds to a constant multiplicative + scale. + + rv_bary_kms : float, optional + Fixed barycentric velocity term in km/s added to the fitted `rv_kms`. + It must use the same standard sign convention as `rv_kms`: positive values + redshift the template/model. + + RV sign convention + ------------------ + The returned rv_kms follows the standard astronomical convention: positive + rv_kms redshifts the template/model spectrum. The legacy + Spyctres.velocity_correction helper is not modified; the observed-grid + compatibility branch wraps it internally so that PHOENIX fitting results use + this convention consistently with native_interp. + + R : float, optional + Resolving power of the Gaussian instrumental line-spread function, + defined as `R = lambda / Delta_lambda_FWHM`. If provided, this is + converted to a constant velocity FWHM and applied after Doppler shifting + and before continuum fitting. + + fwhm_kms : float, optional + Gaussian instrumental FWHM in km/s. Alternative to `R`. Exactly one of + `R` or `fwhm_kms` may be provided. + + forward_model : {"interp_observed", "native_interp"}, optional + Choice of wavelength-space forward-model path. The default preserves the + original observed-grid behavior. The `native_interp` mode keeps the fit + continuous in `(teff, feh, logg)` but uses the native-grid-inspired + shift/convolve/resample-last sequence validated against the X-SHOOTER + PHOENIX notebook reference. + + model_margin_A : float, optional + Wavelength margin in Angstrom used by `forward_model="native_interp"` + when preparing the dense model-space wavelength grid. + + teff_grid, feh_grid, logg_grid : array-like, optional + PHOENIX parameter grids to use when building the interpolator. If not + provided, defaults are chosen by the caller or PHOENIX helper logic. + + cache_path : str, optional + Path to an `.npz` cache file for the PHOENIX interpolator built on the + current model wavelength grid. For `interp_observed` this is the + observed support grid; for `native_interp` it is the dense model-space + wavelength grid. + + allow_missing : bool, optional + If True, allow missing PHOENIX templates when building the interpolator. + Missing grid points are filled with NaNs and may degrade interpolation. + + rv_init : {"grid", None}, optional + Strategy for initializing the radial velocity: + - `"grid"`: perform a coarse RV scan and use the best value to seed the fit + - `None`: use the RV value from `p0` directly + + rv_grid_n : int, optional + Number of trial RV points in the coarse initialization grid when + `rv_init="grid"`. + + rv_grid_decimate : int, optional + Decimation factor used during the coarse RV scan to accelerate the + initialization step. + + x_scale : array-like or str, optional + Passed to `scipy.optimize.least_squares` as the parameter scaling. + + verbose : int, optional + Verbosity level passed to the optimizer. + + max_nfev : int, optional + Maximum number of function evaluations for the nonlinear optimizer. + + Returns + ------- + result : dict + Dictionary with keys: + - `p_best`: best-fit parameter array `[teff, feh, logg, rv_kms]` + - `teff`, `feh`, `logg`, `rv_kms`: best-fit parameters + - `chi2`, `chi2_red`: chi-square and reduced chi-square + - `success`, `status`, `message`: optimizer status information + - `resolution_R`: resolving power used for instrumental broadening, if any + - `lsf_fwhm_kms`: Gaussian LSF FWHM in km/s, if any + - `segment_names`, `segment_weights`, `collection_name`, `collection_meta` + """ + segments, segment_weights, collection_name, collection_meta = _coerce_segments_input(segments) + + if forward_model not in ("interp_observed", "native_interp"): + raise ValueError("forward_model must be 'interp_observed' or 'native_interp'.") + + ( + support_wave_all, + flux_all, + err_all, + support_slices, + fit_slices, + fit_masks, + fit_weights, + seg_meta, + ) = _build_data_vectors( + segments, + segment_weights=segment_weights, + regions=regions, + exclude_regions=exclude_regions, + exclude_mask=exclude_mask, + ) + if support_wave_all.size == 0 or flux_all.size == 0: + raise ValueError("No data points selected for fitting.") + + forward_segments = _make_forward_segments( + segments=segments, + support_wave_all=support_wave_all, + support_slices=support_slices, + fit_masks=fit_masks, + ) + + segment_fwhm_kms = [ + _resolve_segment_fwhm_kms(seg, R=R, fwhm_kms=fwhm_kms) + for seg in forward_segments + ] + + teff0, feh0, logg0, rv0 = map(float, p0) + # Materialize the requested PHOENIX subgrid before deciding whether the + # current interpolator can be reused. + if teff_grid is None: + teff_grid_req = _pick_subgrid( + phoenix_lib.DEFAULT_TEFF_GRID, teff0, half_width=800.0, n_min=5, n_max=9 + ) + else: + teff_grid_req = np.asarray(teff_grid, dtype=float) + + if feh_grid is None: + feh_grid_req = _pick_subgrid( + phoenix_lib.DEFAULT_FEH_GRID, feh0, half_width=0.75, n_min=3, n_max=5 + ) + else: + feh_grid_req = np.asarray(feh_grid, dtype=float) + + if logg_grid is None: + logg_grid_req = _pick_subgrid( + phoenix_lib.DEFAULT_LOGG_GRID, logg0, half_width=0.75, n_min=3, n_max=5 + ) + else: + logg_grid_req = np.asarray(logg_grid, dtype=float) + + if forward_model == "interp_observed": + model_wave_grid = support_wave_all + + segment_media = sorted(set(str(seg.wave_medium).lower() for seg in segments)) + if len(segment_media) == 1: + model_wave_medium = segment_media[0] + else: + model_wave_medium = None + else: + model_wave_grid, model_wave_medium = build_native_interp_wave_grid_for_segments( + segments=forward_segments, + phoenix_lib=phoenix_lib, + model_margin_A=model_margin_A, + ) + + need_rebuild = False + + if phoenix_lib.wave is None: + need_rebuild = True + elif (len(phoenix_lib.wave) != len(model_wave_grid)) or ( + not np.allclose(phoenix_lib.wave, model_wave_grid, rtol=0.0, atol=0.0) + ): + need_rebuild = True + elif phoenix_lib._grid is None: + need_rebuild = True + else: + tg, zg, gg = phoenix_lib._grid + if ( + (len(tg) != len(teff_grid_req)) or + (len(zg) != len(feh_grid_req)) or + (len(gg) != len(logg_grid_req)) or + (not np.allclose(tg, teff_grid_req, rtol=0.0, atol=0.0)) or + (not np.allclose(zg, feh_grid_req, rtol=0.0, atol=0.0)) or + (not np.allclose(gg, logg_grid_req, rtol=0.0, atol=0.0)) + ): + need_rebuild = True + + if need_rebuild: + phoenix_lib.build_interpolator( + observed_wave=model_wave_grid, + teff_grid=teff_grid_req, + feh_grid=feh_grid_req, + logg_grid=logg_grid_req, + cache_path=cache_path, + allow_missing=allow_missing, + observed_wave_medium=model_wave_medium, + ) + + # Set default bounds from the interpolator grid if none supplied + if bounds is None: + bounds = ( + ( + float(np.min(teff_grid_req)), + float(np.min(feh_grid_req)), + float(np.min(logg_grid_req)), + -300.0, + ), + ( + float(np.max(teff_grid_req)), + float(np.max(feh_grid_req)), + float(np.max(logg_grid_req)), + +300.0, + ), + ) + + broadening_fwhm_kms = _resolve_broadening_fwhm_kms(R=R, fwhm_kms=fwhm_kms) + + def residuals(p): + teff, feh, logg, rv_kms = float(p[0]), float(p[1]), float(p[2]), float(p[3]) + rv_tot = rv_bary_kms + rv_kms + + try: + model0 = phoenix_lib.evaluate(teff, feh, logg) + except Exception: + return np.ones_like(flux_all) * 1e6 + + out = np.empty_like(flux_all) + + if forward_model == "interp_observed": + shifted = _apply_observed_grid_rv_shift(support_wave_all, model0, rv_tot) + + for support_sl, fit_sl, fit_mask, seg_weight, seg_fwhm in zip( + support_slices, fit_slices, fit_masks, fit_weights, segment_fwhm_kms + ): + w_support = support_wave_all[support_sl] + m_support = _gaussian_broaden_velocity( + w_support, + shifted[support_sl], + fwhm_kms=seg_fwhm, + ) + + w = w_support[fit_mask] + f = flux_all[fit_sl] + e = err_all[fit_sl] + m = m_support[fit_mask] + + m_corr, coeffs = _solve_multiplicative_legendre(w, f, e, m, mdeg=mdeg) + out[fit_sl] = np.sqrt(float(seg_weight)) * (f - m_corr) / e + else: + model_list = build_phoenix_native_models_for_segments( + segments=forward_segments, + phoenix_wave_native=model_wave_grid, + template_flux_native=model0, + rv_kms=rv_kms, + rv_bary_kms=rv_bary_kms, + segment_fwhm_kms=segment_fwhm_kms, + phoenix_wave_medium=model_wave_medium, + model_margin_A=model_margin_A, + bounds_use_fit_mask=True, + extrapolate=True, + ) + + for seg, model_full, fit_sl, fit_mask, seg_weight in zip( + forward_segments, model_list, fit_slices, fit_masks, fit_weights + ): + w_support = np.asarray(seg.wave, dtype=float) + + w = w_support[fit_mask] + f = flux_all[fit_sl] + e = err_all[fit_sl] + m = np.asarray(model_full, dtype=float)[fit_mask] + + m_corr, coeffs = _solve_multiplicative_legendre(w, f, e, m, mdeg=mdeg) + out[fit_sl] = np.sqrt(float(seg_weight)) * (f - m_corr) / e + + return out + + # RV initialization by coarse grid scan (optional) + if rv_init == "grid": + rv_lo, rv_hi = float(bounds[0][3]), float(bounds[1][3]) + rv_grid = np.linspace(rv_lo, rv_hi, int(rv_grid_n)) + + chi2s = np.empty(rv_grid.size, dtype=float) + for j, rv in enumerate(rv_grid): + if forward_model == "interp_observed": + chi2s[j] = _chi2_for_params( + support_wave_all, + flux_all, + err_all, + support_slices, + fit_slices, + fit_masks, + fit_weights, + teff0, + feh0, + logg0, + rv_bary_kms + float(rv), + phoenix_lib, + mdeg=mdeg, + decimate=rv_grid_decimate, + segment_fwhm_kms=segment_fwhm_kms, + ) + else: + chi2s[j] = _chi2_for_params_native_interp( + forward_segments=forward_segments, + flux_all=flux_all, + err_all=err_all, + fit_slices=fit_slices, + fit_masks=fit_masks, + segment_weights=fit_weights, + teff=teff0, + feh=feh0, + logg=logg0, + rv_tot=rv_bary_kms + float(rv), + phoenix_lib=phoenix_lib, + model_wave_grid=model_wave_grid, + model_wave_medium=model_wave_medium, + mdeg=mdeg, + decimate=rv_grid_decimate, + segment_fwhm_kms=segment_fwhm_kms, + model_margin_A=model_margin_A, + ) + rv0_best = float(rv_grid[int(np.argmin(chi2s))]) + if verbose: + print("RV init grid best:", rv0_best) + p0 = (teff0, feh0, logg0, rv0_best) + elif rv_init is None: + p0 = (teff0, feh0, logg0, rv0) + else: + raise ValueError("rv_init must be 'grid' or None.") + + if x_scale is None: + x_scale = np.array([100.0, 0.1, 0.1, 10.0], dtype=float) + + res = least_squares( + residuals, + x0=np.array(p0, dtype=float), + bounds=bounds, + method="trf", + x_scale=x_scale, + max_nfev=int(max_nfev), + verbose=2 if verbose else 0, + ) + + # Compute diagnostics. If segment weights are used, chi2 is the weighted + # sum of squared normalized residuals. + r = res.fun + chi2 = float(np.sum(r * r)) + n = int(r.size) + k = 4 # teff, feh, logg, rv + # Effective dof includes polynomial coefficients, but they were solved analytically. + # Report dof as N - k for a conservative baseline. + dof = max(1, n - k) + chi2_red = chi2 / dof + + return { + "success": bool(res.success), + "message": res.message, + "p_best": res.x, + "teff": float(res.x[0]), + "feh": float(res.x[1]), + "logg": float(res.x[2]), + "rv_kms": float(res.x[3]), + "rv_bary_kms": float(rv_bary_kms), + "chi2": chi2, + "dof": dof, + "chi2_red": chi2_red, + "n_points": n, + "status": int(res.status), + "nfev": int(res.nfev), + "seg_meta": seg_meta, + "forward_model": str(forward_model), + "model_margin_A": float(model_margin_A), + "n_segments": int(len(seg_meta)), + "segment_names": [m.get("name") for m in seg_meta], + "segment_weights": [float(w) for w in fit_weights], + "collection_name": collection_name, + "collection_meta": collection_meta, + "segment_lsf_fwhm_kms": [ + None if x is None else float(x) for x in segment_fwhm_kms + ], + "segment_resolution_R_effective": [ + None if x is None else float(C_KMS / x) for x in segment_fwhm_kms + ], + # Backward-compatible global broadening metadata. + # The actual per-segment broadening used in the fit is stored above in + # segment_lsf_fwhm_kms and segment_resolution_R_effective. + "resolution_R": None if R is None else float(R), + "lsf_fwhm_kms": None if broadening_fwhm_kms is None else float(broadening_fwhm_kms), + # Note: did not store poly coeffs in this minimal version to avoid re-evaluating. + } diff --git a/Spyctres/io.py b/Spyctres/io.py new file mode 100644 index 0000000..45fb05b --- /dev/null +++ b/Spyctres/io.py @@ -0,0 +1,1108 @@ +# Spyctres/io.py +import io as _pyio +import os +import re +import numpy as np +from astropy.io import fits + + +class SpectrumSegment(object): + """ + Minimal internal spectrum container. + + wave: 1D array (Angstrom by convention unless specified) + flux: 1D array + err: 1D array (1-sigma), optional + mask: boolean array (True = use), optional + meta: dict, optional + wave_medium: "air", "vacuum", or "unknown" + wave_frame: "topocentric", "heliocentric", "barycentric", "stellar_rest", or "unknown" + """ + + def __init__( + self, + wave, + flux, + err=None, + mask=None, + meta=None, + wave_medium="unknown", + wave_frame="unknown", + name=None, + ): + self.wave = np.asarray(wave, dtype=float) + self.flux = np.asarray(flux, dtype=float) + + if self.wave.ndim != 1 or self.flux.ndim != 1: + raise ValueError("wave and flux must be 1D arrays.") + if self.wave.shape[0] != self.flux.shape[0]: + raise ValueError("wave and flux must have the same length.") + + if err is not None: + self.err = np.asarray(err, dtype=float) + if self.err.ndim != 1 or self.err.shape[0] != self.wave.shape[0]: + raise ValueError("err must be 1D and match wave length.") + else: + self.err = None + + if mask is None: + m = np.isfinite(self.wave) & np.isfinite(self.flux) + if self.err is not None: + m &= np.isfinite(self.err) & (self.err > 0) + self.mask = m + else: + self.mask = np.asarray(mask, dtype=bool) + if self.mask.ndim != 1 or self.mask.shape[0] != self.wave.shape[0]: + raise ValueError("mask must be 1D and match wave length.") + + self.meta = {} if meta is None else dict(meta) + self.wave_medium = str(wave_medium).strip().lower() + self.wave_frame = str(wave_frame).strip().lower() + self.name = name + + def copy( + self, + wave=None, + flux=None, + err=None, + mask=None, + meta=None, + wave_medium=None, + wave_frame=None, + name=None, + ): + return SpectrumSegment( + self.wave if wave is None else wave, + self.flux if flux is None else flux, + self.err if err is None else err, + self.mask if mask is None else mask, + meta=self.meta if meta is None else meta, + wave_medium=self.wave_medium if wave_medium is None else wave_medium, + wave_frame=self.wave_frame if wave_frame is None else wave_frame, + name=self.name if name is None else name, + ) + + def sorted(self): + idx = np.argsort(self.wave) + return self.copy( + wave=self.wave[idx], + flux=self.flux[idx], + err=None if self.err is None else self.err[idx], + mask=self.mask[idx], + ) + + def subset(self, selector, name=None, name_suffix=None): + """ + Return a subsetted copy of the segment. + + selector may be: + - a boolean mask with the same length as the segment, or + - an integer/slice indexer understood by NumPy. + """ + if isinstance(selector, slice): + idx = selector + else: + selector = np.asarray(selector) + if selector.dtype == bool: + if selector.ndim != 1 or selector.shape[0] != self.wave.shape[0]: + raise ValueError("Boolean selector must be 1D and match wave length.") + idx = selector + else: + idx = selector + + out_name = self.name if name is None else name + if name_suffix is not None: + base = "" if out_name is None else str(out_name) + out_name = "{0}_{1}".format(base, name_suffix) if base else str(name_suffix) + + return self.copy( + wave=self.wave[idx], + flux=self.flux[idx], + err=None if self.err is None else self.err[idx], + mask=self.mask[idx], + meta=dict(self.meta), + name=out_name, + ) + + def window(self, wmin=None, wmax=None, clip_left=0, clip_right=0, name=None, name_suffix=None): + """ + Return a wavelength-windowed copy of the segment. + + The wavelength cut is inclusive in [wmin, wmax]. After that, optional + pixel clipping is applied on the left/right edges of the retained block. + """ + keep = np.ones_like(self.wave, dtype=bool) + if wmin is not None: + keep &= (self.wave >= float(wmin)) + if wmax is not None: + keep &= (self.wave <= float(wmax)) + + idx = np.where(keep)[0] + if idx.size == 0: + raise ValueError("No points remain after wavelength windowing.") + + i0 = idx[0] + i1 = idx[-1] + 1 + + out = self.subset(slice(i0, i1), name=name, name_suffix=name_suffix) + + if clip_left > 0 or clip_right > 0: + n = len(out.wave) + j0 = int(max(0, clip_left)) + j1 = int(n - max(0, clip_right)) + if j1 <= j0: + raise ValueError("Edge clipping removed all points.") + out = out.subset(slice(j0, j1), name=out.name) + + return out + + def with_wave(self, wave, wave_medium=None, wave_frame=None, name=None, name_suffix=None): + """ + Return a copy with a replaced wavelength array and optionally updated + wavelength metadata. Flux, err, and mask are preserved. + """ + out_name = self.name if name is None else name + if name_suffix is not None: + base = "" if out_name is None else str(out_name) + out_name = "{0}_{1}".format(base, name_suffix) if base else str(name_suffix) + + return self.copy( + wave=np.asarray(wave, dtype=float), + meta=dict(self.meta), + wave_medium=self.wave_medium if wave_medium is None else wave_medium, + wave_frame=self.wave_frame if wave_frame is None else wave_frame, + name=out_name, + ) + + +class SpectrumCollection(object): + """ + Thin container for a joint fit over multiple SpectrumSegment objects. + + Parameters + ---------- + segments : SpectrumSegment or sequence of SpectrumSegment + Segment objects to be grouped together. + weights : array-like, optional + Positive per-segment weights used by joint fitting. If None, all + segments receive unit weight. + meta : dict, optional + Optional collection-level metadata. + name : str, optional + Optional collection name. + """ + + def __init__(self, segments, weights=None, meta=None, name=None): + if isinstance(segments, SpectrumSegment): + segments = [segments] + else: + segments = list(segments) + + if len(segments) == 0: + raise ValueError("SpectrumCollection requires at least one SpectrumSegment.") + + for i, seg in enumerate(segments): + if not isinstance(seg, SpectrumSegment): + raise TypeError( + "All entries in SpectrumCollection must be SpectrumSegment objects; " + "got type {0} at index {1}.".format(type(seg).__name__, i) + ) + + self.segments = list(segments) + + if weights is None: + self.weights = np.ones(len(self.segments), dtype=float) + else: + w = np.asarray(weights, dtype=float) + if w.ndim != 1 or len(w) != len(self.segments): + raise ValueError("weights must be 1D and match the number of segments.") + if not np.all(np.isfinite(w)): + raise ValueError("weights must be finite.") + if np.any(w <= 0): + raise ValueError("weights must be > 0.") + self.weights = np.array(w, copy=True, dtype=float) + + self.meta = {} if meta is None else dict(meta) + self.name = name + + def __len__(self): + return len(self.segments) + + def __iter__(self): + return iter(self.segments) + + def __getitem__(self, item): + return self.segments[item] + + def copy(self, segments=None, weights=None, meta=None, name=None): + return SpectrumCollection( + list(self.segments) if segments is None else segments, + weights=np.array(self.weights, copy=True) if weights is None else weights, + meta=self.meta if meta is None else meta, + name=self.name if name is None else name, + ) + + @property + def names(self): + return [seg.name for seg in self.segments] + + +def concatenate_segments(segments, sort=True, name=None): + """ + Concatenate multiple SpectrumSegment objects into a single SpectrumSegment. + + If all input segments share the same wavelength medium and frame, those are + preserved. Otherwise they are set to "unknown" on the merged segment. + """ + segments = list(segments) + if len(segments) == 0: + raise ValueError("concatenate_segments requires at least one segment.") + + wave = np.concatenate([s.wave for s in segments]) + flux = np.concatenate([s.flux for s in segments]) + + if any(s.err is None for s in segments): + err = None + else: + err = np.concatenate([s.err for s in segments]) + + mask = np.concatenate([s.mask for s in segments]) + + media = {str(s.wave_medium).lower() for s in segments} + frames = {str(s.wave_frame).lower() for s in segments} + wave_medium = next(iter(media)) if len(media) == 1 else "unknown" + wave_frame = next(iter(frames)) if len(frames) == 1 else "unknown" + + meta = { + "n_segments": len(segments), + "segment_names": [s.name for s in segments], + "wave_medium": wave_medium, + "wave_frame": wave_frame, + } + out = SpectrumSegment( + wave, + flux, + err=err, + mask=mask, + meta=meta, + wave_medium=wave_medium, + wave_frame=wave_frame, + name=name, + ) + return out.sorted() if sort else out + + +def make_window_segments(seg, windows, pad=0.0, name_prefix=None): + """ + Build one SpectrumSegment per wavelength window. + + Parameters + ---------- + seg : SpectrumSegment + Input segment. + windows : sequence + Each entry may be either (wmin, wmax) or (label, wmin, wmax). + pad : float, optional + Extra wavelength padding added on both sides of each window. + name_prefix : str, optional + Prefix used when a window does not provide its own label. + + Returns + ------- + list of SpectrumSegment + """ + out = [] + for i, item in enumerate(windows): + if len(item) == 2: + wmin, wmax = item + label = None + elif len(item) == 3: + label, wmin, wmax = item + else: + raise ValueError("Each window must be (wmin, wmax) or (label, wmin, wmax).") + + wmin_pad = float(wmin) - float(pad) + wmax_pad = float(wmax) + float(pad) + + if label is not None: + name = str(label) + elif name_prefix is not None: + name = "{0}_{1}".format(name_prefix, i + 1) + else: + name = seg.name + + out.append(seg.window(wmin=wmin_pad, wmax=wmax_pad, name=name)) + + return out + + +def make_padded_window_segments(seg, windows, pad=5.0, name_prefix=None): + """ + Build one SpectrumSegment per wavelength window with padded support. + + The returned segment covers the support region [wmin-pad, wmax+pad], but + seg.mask is True only on the inner fit window [wmin, wmax]. This lets the + fitter evaluate models on a padded support region while computing chi-square + only on the inner fit pixels. + + Parameters + ---------- + seg : SpectrumSegment + Input segment. + windows : sequence + Each entry may be either (wmin, wmax) or (label, wmin, wmax). + pad : float, optional + Extra wavelength padding added on both sides of each fit window. + name_prefix : str, optional + Prefix used when a window does not provide its own label. + + Returns + ------- + list of SpectrumSegment + """ + wave = np.asarray(seg.wave, dtype=float) + flux = np.asarray(seg.flux, dtype=float) + err = None if seg.err is None else np.asarray(seg.err, dtype=float) + base_mask = np.asarray(seg.mask, dtype=bool) + + out = [] + for i, item in enumerate(windows): + if len(item) == 2: + wmin, wmax = item + label = None + elif len(item) == 3: + label, wmin, wmax = item + else: + raise ValueError("Each window must be (wmin, wmax) or (label, wmin, wmax).") + + support_lo = float(wmin) - float(pad) + support_hi = float(wmax) + float(pad) + + keep = (wave >= support_lo) & (wave <= support_hi) + if not np.any(keep): + continue + + fit_mask = base_mask[keep] & (wave[keep] >= float(wmin)) & (wave[keep] <= float(wmax)) + + if label is not None: + name = str(label) + elif name_prefix is not None: + base = "" if seg.name is None else str(seg.name) + name = "{0}_{1}_win{2}".format(base, name_prefix, i) if base else "{0}_win{1}".format(name_prefix, i) + else: + base = "" if seg.name is None else str(seg.name) + name = "{0}_win{1}".format(base, i) if base else "win{0}".format(i) + + out.append( + SpectrumSegment( + wave=wave[keep], + flux=flux[keep], + err=None if err is None else err[keep], + mask=fit_mask, + meta=dict(seg.meta), + wave_medium=seg.wave_medium, + wave_frame=seg.wave_frame, + name=name, + ) + ) + + if len(out) == 0: + raise ValueError("No points remain after applying padded windows.") + + return out + + + +def _pepsi_fiber_to_resolution(fiber): + """ + PEPSI nominal resolving power by science fiber diameter. + 100 um -> 250000 + 200 um -> 130000 + 300 um -> 50000 + """ + if fiber is None: + return None + + digits = "".join(ch for ch in str(fiber) if ch.isdigit()) + mapping = { + "100": 250000.0, + "200": 130000.0, + "300": 50000.0, + } + return mapping.get(digits, None) + + +def _normalize_hdu_token(value): + """ + Normalize FITS extension-like names for robust matching. + + Examples + -------- + 'ORD13_ERRS' -> 'ERRS' + 'ORD16_QUAL' -> 'QUAL' + 'FLUX' -> 'FLUX' + """ + if value is None: + return None + + s = str(value).strip().upper() + if not s: + return None + + parts = s.split("_") + + # Common X-SHOOTER merged-1D pattern: ORDxx_ERRS / ORDxx_QUAL + if len(parts) >= 2 and parts[-1] in ["FLUX", "ERRS", "QUAL"]: + return parts[-1] + + return s + + +def _find_hdu_by_name(hdul, extname): + """ + Return HDU matching EXTNAME, or None if not found. + + Matching is tolerant of logical product names such as ORD13_ERRS + versus actual EXTNAME='ERRS'. + """ + target = _normalize_hdu_token(extname) + if target is None: + return None + + for hdu in hdul: + name = _normalize_hdu_token(hdu.header.get("EXTNAME", None)) + if name == target: + return hdu + + return None + + +def _resolve_hdu(hdul, ref): + """ + Resolve an HDU either by integer index or by extension-name-like string. + + For string references, first try EXTNAME matching, then also allow + matches against SCIDATA / ERRDATA / QUALDATA header pointers after + normalization. + """ + if ref is None: + return None + + if isinstance(ref, (int, np.integer)): + return hdul[int(ref)] + + target = _normalize_hdu_token(ref) + + hdu = _find_hdu_by_name(hdul, target) + if hdu is not None: + return hdu + + for hdu in hdul: + hdr = hdu.header + aliases = [ + hdr.get("EXTNAME"), + hdr.get("SCIDATA"), + hdr.get("ERRDATA"), + hdr.get("QUALDATA"), + ] + aliases = [_normalize_hdu_token(x) for x in aliases] + if target in aliases: + return hdu + + raise ValueError("Could not find FITS extension '{0}'.".format(ref)) + + +def _build_linear_wave_from_header(hdr, n_pix): + """ + Build a linear wavelength array from CRVAL1/CDELT1/CRPIX1. + """ + if "CRVAL1" not in hdr or "CDELT1" not in hdr: + raise ValueError("Header is missing CRVAL1/CDELT1 needed for wavelength solution.") + + crval1 = float(hdr["CRVAL1"]) + cdelt1 = float(hdr["CDELT1"]) + crpix1 = float(hdr.get("CRPIX1", 1.0)) + + i = np.arange(int(n_pix), dtype=float) + return crval1 + (i + 1.0 - crpix1) * cdelt1 + + +def _wave_to_angstrom(wave, unit): + """ + Convert wavelength array to Angstrom. + """ + wave = np.asarray(wave, dtype=float) + u = "" if unit is None else str(unit).strip().lower() + + if u in ["a", "aa", "angstrom", "angstroms", "ang"]: + return wave + + if u in ["nm", "nanometer", "nanometers"]: + return wave * 10.0 + + if u in ["um", "micron", "microns", "micrometer", "micrometers"]: + return wave * 1.0e4 + + raise ValueError("Unsupported wavelength unit '{0}'.".format(unit)) + + +def _xshooter_slit_keyword_for_arm(arm): + """ + Map X-SHOOTER arm to the slit keyword used in these headers. + """ + arm = str(arm).strip().upper() + mapping = { + "UVB": "HIERARCH ESO INS OPTI3 NAME", + "VIS": "HIERARCH ESO INS OPTI4 NAME", + "NIR": "HIERARCH ESO INS OPTI5 NAME", + } + return mapping.get(arm, None) + + +def _xshooter_parse_slit_width(slit_name): + """ + Parse slit width in arcsec from strings like '1.0x11', '0.7x11', '0.6x11'. + Returns float or the string 'IFU', or None. + """ + if slit_name is None: + return None + + s = str(slit_name).strip().upper() + + if not s: + return None + + if "IFU" in s: + return "IFU" + + # Take the first numeric token, which is the slit width in strings like 1.0x11 + m = re.match(r"^\s*([0-9]+(?:\.[0-9]+)?)", s) + if m is not None: + return float(m.group(1)) + + return None + + +def _xshooter_resolution_from_slit(arm, slit_name): + """ + Nominal X-SHOOTER resolving power as a function of arm and slit width. + Values follow the X-SHOOTER user manual. + """ + arm = str(arm).strip().upper() + slit = _xshooter_parse_slit_width(slit_name) + + table = { + "UVB": {0.5: 9700.0, 0.8: 6700.0, 1.0: 5400.0, 1.3: 4100.0, 1.6: 3300.0, "IFU": 7900.0}, + "VIS": {0.4: 17400.0, 0.7: 11000.0, 0.9: 8800.0, 1.2: 6700.0, 1.5: 5400.0, "IFU": 12600.0}, + "NIR": {0.4: 11300.0, 0.6: 8100.0, 0.9: 5600.0, 1.2: 4300.0, "IFU": 8100.0}, + } + + if slit is None or arm not in table: + return None + + if slit == "IFU": + return table[arm].get("IFU") + + widths = [k for k in table[arm].keys() if k != "IFU"] + if len(widths) == 0: + return None + + # Use nearest supported slit width to be robust against header formatting quirks + nearest = min(widths, key=lambda w: abs(float(w) - float(slit))) + + if abs(float(nearest) - float(slit)) > 0.051: + return None + + return table[arm][nearest] + + +def _xshooter_telluric_corrected(hdr): + """ + Heuristic flag for telluric-corrected X-SHOOTER products. + """ + prodcatg = str(hdr.get("HIERARCH ESO PRO CATG", "")).upper() + pipefile = str(hdr.get("PIPEFILE", "")).upper() + + return ("TELLURIC" in prodcatg) or ("TELLURIC" in pipefile) + + +def read_pepsi_nor( + path, + ext=1, + wave_col="Arg", + flux_col="Fun", + var_col="Var", + wave_medium="unknown", + wave_frame="unknown", + infer_resolution=True, +): + """ + Read PEPSI .nor files (FITS binary table). + + Expected columns: Arg, Fun, Var (variance). + No wavelength-medium or reference-frame conversion is applied here. + Those are carried as metadata for later handling in the fitter or utilities. + """ + path = os.path.abspath(os.path.expanduser(path)) + with fits.open(path, memmap=False) as hdul: + data = hdul[ext].data + cols = set(c.name for c in hdul[ext].columns) + + if wave_col not in cols or flux_col not in cols: + raise ValueError( + "Missing required columns in {0}: found {1}".format(path, sorted(cols)) + ) + + wave = np.array(data[wave_col], dtype=float) + flux = np.array(data[flux_col], dtype=float) + + err = None + if var_col in cols: + var = np.array(data[var_col], dtype=float) + err = np.sqrt(var) + + phdr = hdul[0].header if len(hdul) > 0 else fits.Header() + ehdr = hdul[ext].header + + def get_any(key, default=None): + if key in ehdr: + return ehdr[key] + if key in phdr: + return phdr[key] + return default + + meta = { + "path": path, + "ext": ext, + "columns": sorted(cols), + "instrument": get_any("INSTRUME"), + "object": get_any("OBJECT"), + "arm": get_any("ARM"), + "fiber": get_any("FIBER"), + "cross_disperser": get_any("CROSDIS"), + "date_obs": get_any("DATE-OBS"), + "time_obs": get_any("TIME-OBS"), + "exptime": get_any("EXPTIME"), + "jd_obs": get_any("JD-OBS"), + "jd_tdb": get_any("JD-TDB"), + "ra": get_any("RA"), + "dec": get_any("DEC"), + "ra2000": get_any("RA2000"), + "dec2000": get_any("DE2000"), + "ssbvel_mps": get_any("SSBVEL"), + "charave_mps": get_any("CHARAVE"), + "was_file": get_any("WAS"), + "trace_file": get_any("TRACE"), + "wasrep": get_any("WASREP"), + "wasfwhm_pix": get_any("WASFWHM"), + "wave_medium": wave_medium, + "wave_frame": wave_frame, + } + + resolution_R = None + if infer_resolution: + wasrep = meta.get("wasrep") + if wasrep is not None: + try: + resolution_R = float(wasrep) + except Exception: + resolution_R = None + + if resolution_R is None: + resolution_R = _pepsi_fiber_to_resolution(meta.get("fiber")) + + meta["resolution_R"] = resolution_R + + name = os.path.basename(path) + return SpectrumSegment( + wave, + flux, + err=err, + meta=meta, + wave_medium=wave_medium, + wave_frame=wave_frame, + name=name, + ).sorted() + + +def read_xshooter_1d( + path, + flux_ext=0, + err_ext=None, + qual_ext=None, + wave_unit=None, + wave_medium="air", + wave_frame="topocentric", + infer_resolution=True, +): + """ + Read a merged 1D X-SHOOTER spectrum stored as a linear FITS image product. + + Assumptions for the current implementation: + - flux is in the primary HDU by default + - wavelength is reconstructed from CRVAL1/CDELT1/CRPIX1 + - error and quality HDUs are resolved from ERRDATA / QUALDATA when present + - no air/vacuum or barycentric correction is applied here; those are carried as metadata + """ + path = os.path.abspath(os.path.expanduser(path)) + + with fits.open(path, memmap=False) as hdul: + flux_hdu = _resolve_hdu(hdul, flux_ext) + phdr = flux_hdu.header + flux = np.asarray(flux_hdu.data, dtype=float) + + if flux.ndim != 1: + raise ValueError("X-SHOOTER reader expects a 1D flux array in the selected HDU.") + + err_ref = err_ext if err_ext is not None else phdr.get("ERRDATA", 1) + qual_ref = qual_ext if qual_ext is not None else phdr.get("QUALDATA", 2) + + err_hdu = _resolve_hdu(hdul, err_ref) + qual_hdu = _resolve_hdu(hdul, qual_ref) + + err = None if err_hdu is None else np.asarray(err_hdu.data, dtype=float) + qual = None if qual_hdu is None else np.asarray(qual_hdu.data) + + if err is not None and err.shape != flux.shape: + raise ValueError("Error array shape does not match flux array shape.") + if qual is not None and qual.shape != flux.shape: + raise ValueError("Quality array shape does not match flux array shape.") + + raw_wave = _build_linear_wave_from_header(phdr, flux.size) + + cunit1 = phdr.get("CUNIT1", None) + unit_in = wave_unit if wave_unit is not None else cunit1 + if unit_in is None: + unit_in = "nm" + + wave = _wave_to_angstrom(raw_wave, unit_in) + + arm = str(phdr.get("HIERARCH ESO SEQ ARM", phdr.get("ARM", "unknown"))).strip().upper() + slit_key = _xshooter_slit_keyword_for_arm(arm) + slit_name = phdr.get(slit_key, None) if slit_key is not None else None + + resolution_R = None + if infer_resolution: + resolution_R = _xshooter_resolution_from_slit(arm, slit_name) + + mask = np.isfinite(wave) & np.isfinite(flux) + + if err is not None: + mask &= np.isfinite(err) & (err > 0) + + if qual is not None: + mask &= (np.asarray(qual) == 0) + + barycorr_kms = phdr.get("HIERARCH ESO QC VRAD BARYCOR", None) + helicorr_kms = phdr.get("HIERARCH ESO QC VRAD HELICOR", None) + + meta = { + "path": path, + "instrument": phdr.get("INSTRUME", "XSHOOTER"), + "object": phdr.get("OBJECT"), + "arm": arm, + "mode": phdr.get("HIERARCH ESO INS MODE"), + "slit_keyword": slit_key, + "slit_name": slit_name, + "slit_width_arcsec": _xshooter_parse_slit_width(slit_name), + "resolution_R": resolution_R, + "date_obs": phdr.get("DATE-OBS"), + "mjd_obs": phdr.get("MJD-OBS"), + "exptime": phdr.get("EXPTIME"), + "ra": phdr.get("RA"), + "dec": phdr.get("DEC"), + "bunit": phdr.get("BUNIT"), + "cunit1": cunit1, + "wave_unit_input": unit_in, + "prodcatg": phdr.get("HIERARCH ESO PRO CATG"), + "pipefile": phdr.get("PIPEFILE"), + "err_ext": err_ref, + "qual_ext": qual_ref, + "barycorr_kms": barycorr_kms, + "helicorr_kms": helicorr_kms, + "telluric_corrected": _xshooter_telluric_corrected(phdr), + "wave_medium": wave_medium, + "wave_frame": wave_frame, + } + + name = os.path.basename(path) + + return SpectrumSegment( + wave, + flux, + err=err, + mask=mask, + meta=meta, + wave_medium=wave_medium, + wave_frame=wave_frame, + name=name, + ).sorted() + +def read_floyds_csv(path, name=None): + """ + Read a reduced 1D FLOYDS spectrum from a simple ASCII/CSV export. + + Expected format: + - optional comment lines beginning with '#' + - one header line with column names such as 'wavelength flux' + - numeric rows thereafter + + Returns + ------- + SpectrumSegment + """ + path = os.path.abspath(os.path.expanduser(path)) + + with open(path, "r", encoding="utf-8", errors="replace") as f: + lines = f.readlines() + + comments = {} + for line in lines: + if not line.lstrip().startswith("#"): + continue + body = line.lstrip()[1:].strip() + if ":" in body: + k, v = body.split(":", 1) + comments[k.strip().lower()] = v.strip() + + data_text = "".join( + ln for ln in lines + if ln.strip() and not ln.lstrip().startswith("#") + ) + if not data_text.strip(): + raise ValueError("No tabular data found in FLOYDS file: {0}".format(path)) + + arr = np.genfromtxt( + _pyio.StringIO(data_text), + names=True, + dtype=float, + encoding=None, + ) + + if arr.dtype.names is None: + raise ValueError( + "Could not parse named columns from FLOYDS file: {0}".format(path) + ) + + names = list(arr.dtype.names) + names_l = [n.lower() for n in names] + + wave_candidates = ["wavelength", "wave", "lambda", "lam"] + flux_candidates = ["flux", "f_lambda", "flam", "fnu"] + err_candidates = ["err", "error", "uncertainty", "sigma", "flux_err", "fluxerror"] + + def _pick(candidates): + for c in candidates: + if c in names_l: + return names[names_l.index(c)] + return None + + wave_col = _pick(wave_candidates) + flux_col = _pick(flux_candidates) + err_col = _pick(err_candidates) + + if wave_col is None or flux_col is None: + raise ValueError( + "Need wavelength and flux columns in FLOYDS file: {0}. " + "Found columns: {1}".format(path, names) + ) + + wave = np.asarray(arr[wave_col], dtype=float) + flux = np.asarray(arr[flux_col], dtype=float) + err = None if err_col is None else np.asarray(arr[err_col], dtype=float) + + mask = np.isfinite(wave) & np.isfinite(flux) + if err is not None: + mask &= np.isfinite(err) & (err > 0) + + meta = { + "path": path, + "instrument": "FLOYDS", + "facility": comments.get("facility"), + "date_obs": comments.get("date-obs"), + "resolution_R": 500.0, + "resolution_note": "Approximate nominal FLOYDS merged-spectrum value; actual R varies with wavelength and slit.", + "wave_medium": "unknown", + "wave_frame": "unknown", + } + + seg_name = ( + name + or comments.get("object") + or comments.get("target") + or os.path.basename(path) + ) + + return SpectrumSegment( + wave=wave, + flux=flux, + err=err, + mask=mask, + meta=meta, + wave_medium="unknown", + wave_frame="unknown", + name=seg_name, + ).sorted() + + +def read_gemini_gmos_ascii(path, name=None): + """ + Read a reduced 1D Gemini/GMOS spectrum from an IRAF wspectext-like ASCII export. + + Expected format: + - optional FITS-like header cards at the top + - optional END line terminating the header + - numeric rows thereafter, typically wavelength flux + - an optional third numeric column is treated as err + + Returns + ------- + SpectrumSegment + """ + path = os.path.abspath(os.path.expanduser(path)) + + with open(path, "r", encoding="utf-8", errors="replace") as f: + lines = f.readlines() + + header = {} + data_lines = [] + in_header = True + + for line in lines: + s = line.strip() + + if not s: + continue + + if in_header: + if s == "END": + in_header = False + continue + + # FITS-like header card, e.g. KEYWORD = value / comment + if "=" in line and not re.match(r"^[+-]?[0-9]", s): + key, rest = line.split("=", 1) + key = key.strip() + value = rest.split("/", 1)[0].strip() + + if len(value) >= 2 and value[0] == "'" and value[-1] == "'": + value = value[1:-1].strip() + + header[key] = value + continue + + # No explicit END: if the line begins numerically, data start here. + if re.match(r"^[+-]?(?:\d+\.?\d*|\.\d+)(?:[Ee][+-]?\d+)?", s): + in_header = False + data_lines.append(line) + continue + + # Otherwise ignore stray non-data lines in the header block. + continue + + data_lines.append(line) + + if len(data_lines) == 0: + raise ValueError("No numeric spectral data found in Gemini/GMOS ASCII file: {0}".format(path)) + + arr = np.genfromtxt(_pyio.StringIO("".join(data_lines)), dtype=float) + + if arr.ndim == 1: + if arr.size < 2: + raise ValueError("Need at least two numeric columns (wave, flux) in: {0}".format(path)) + arr = arr.reshape(1, -1) + + if arr.ndim != 2 or arr.shape[1] < 2: + raise ValueError( + "Could not parse Gemini/GMOS ASCII spectrum with at least two columns: {0}".format(path) + ) + + wave = np.asarray(arr[:, 0], dtype=float) + flux = np.asarray(arr[:, 1], dtype=float) + err = None + if arr.shape[1] >= 3: + err = np.asarray(arr[:, 2], dtype=float) + + mask = np.isfinite(wave) & np.isfinite(flux) + if err is not None: + mask &= np.isfinite(err) & (err > 0) + + meta = { + "path": path, + "instrument": "GMOS", + "facility": "Gemini", + "object": header.get("OBJECT"), + "filename": header.get("FILENAME"), + "origin": header.get("ORIGIN"), + "iraf_type": header.get("IRAFTYPE"), + "wave_unit_input": "angstrom", + "resolution_R": None, + "wave_medium": "unknown", + "wave_frame": "unknown", + "header_cards": dict(header), + } + + seg_name = ( + name + or header.get("OBJECT") + or header.get("FILENAME") + or os.path.basename(path) + ) + + return SpectrumSegment( + wave=wave, + flux=flux, + err=err, + mask=mask, + meta=meta, + wave_medium="unknown", + wave_frame="unknown", + name=seg_name, + ).sorted() + + +READERS = {} + + +def register_reader(names, func): + """ + Register one reader function under one or more instrument aliases. + """ + if isinstance(names, str): + names = [names] + + for name in names: + key = str(name).strip().lower() + if not key: + continue + READERS[key] = func + + +register_reader(["pepsi", "pepsi_nor", "pepsi-1d", "pepsi1d"], read_pepsi_nor) +register_reader(["xshooter", "x-shooter", "xsh", "xshooter_1d", "xshooter-1d"], read_xshooter_1d) +register_reader(["floyds", "floyds_csv", "lco_floyds"], read_floyds_csv) +register_reader(["gemini", "gmos", "gemini_gmos", "gmos_ascii", "gemini_ascii"], read_gemini_gmos_ascii) + + +def read_spectrum(path, instrument=None, **kwargs): + """ + Dispatch to one of the registered 1D spectrum readers. + + Parameters + ---------- + path : str + Input file path. + instrument : str + Reader alias such as "pepsi", "xshooter", "floyds", or "gemini". + **kwargs + Additional reader-specific keyword arguments. + """ + inst = (instrument or "").strip().lower() + func = READERS.get(inst, None) + + if func is None: + raise ValueError( + "Unknown instrument '{0}'. Supported: pepsi, xshooter, floyds, gemini".format(instrument) + ) + + return func(path, **kwargs) diff --git a/Spyctres/phoenix.py b/Spyctres/phoenix.py new file mode 100644 index 0000000..4db7936 --- /dev/null +++ b/Spyctres/phoenix.py @@ -0,0 +1,597 @@ +# Spyctres/phoenix.py +import os +import hashlib +import numpy as np +from astropy.io import fits + +from scipy.interpolate import InterpolatedUnivariateSpline +from scipy.interpolate import RegularGridInterpolator +from .waveutils import convert_wavelength_medium + + +def _as_float_array(x): + x = np.asarray(x, dtype=float) + if x.ndim != 1: + raise ValueError("Expected a 1D wavelength array.") + return x + + +def _format_feh_dir(feh): + # PHOENIX uses Z-0.0 (not Z+0.0). Keep + for other positive metallicities. + feh = float(feh) + s = "{:+.1f}".format(feh) + if abs(feh) < 1e-12: + s = "-0.0" + return "Z{0}".format(s) + + +def _format_feh_file(feh): + feh = float(feh) + s = "{:+.1f}".format(feh) + if abs(feh) < 1e-12: + s = "-0.0" + return s + + +def _format_logg(logg): + return "{:.2f}".format(float(logg)) + + +def _same_float_array(a, b): + """ + Exact array equality for 1D float-like grids used in cache validation. + """ + if a is None or b is None: + return False + + a = _as_float_array(a) + b = _as_float_array(b) + + if len(a) != len(b): + return False + + return np.allclose(a, b, rtol=0.0, atol=0.0) + +def _normalize_cache_string(x): + """ + Normalize scalar strings loaded from npz cache entries. + """ + if x is None: + return None + + if isinstance(x, np.ndarray): + if x.shape == (): + x = x.item() + else: + raise ValueError("Expected a scalar cache string entry.") + + if isinstance(x, bytes): + x = x.decode("utf-8") + + return str(x) + + +def phoenix_relpath(teff, logg, feh, model_tag="PHOENIX-ACES-AGSS-COND-2011-HiRes"): + """ + Return relative path from the template root to a PHOENIX template. + + Layout supported here (your install): + / + WAVE_PHOENIX-ACES-AGSS-COND-2011.fits + Z-0.0/ + lte05000-4.00-0.0.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits + Z-1.0/ + ... + + If you later want to support the alternative nested layout, we can add it, + but for now we match your PHOENIXv2 tree. + """ + teff_i = int(round(float(teff))) + logg_s = _format_logg(logg) + feh_s = _format_feh_file(feh) + zdir = _format_feh_dir(feh) + + fname = "lte{0}-{1}{2}.{3}.fits".format( + str(teff_i).zfill(5), logg_s, feh_s, model_tag + ) + return os.path.join(zdir, fname) + + +class PhoenixLibrary(object): + """ + Minimal PHOENIX template backend. + + Typical base_dir layout (HiResFITS): + base_dir/ + WAVE_PHOENIX-ACES-AGSS-COND-2011.fits + PHOENIX-ACES-AGSS-COND-2011/ + Z-0.0/ + lte05000-4.00-0.0.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits + Z-1.0/ + ... + + Wavelength-grid state: + + self.phoenix_wave + Immutable native PHOENIX wavelength grid loaded from the PHOENIX wavelength + file. This is the high-resolution template grid on disk and should be used + whenever a native-grid forward model needs to be constructed. + + self.wave + Current interpolator/evaluation grid. This is set by build_interpolator(). + It may be an observed support grid for interp_observed, or a clipped + model-space grid for native_interp. It should not be treated as the native + PHOENIX grid. + + This class supports: + - Loading a single template (and optionally resampling to a target wave grid) + - Building a cached regular-grid interpolator on (Teff, [Fe/H], logg) + """ + + DEFAULT_TEFF_GRID = np.array([ + 2300., 2400., 2500., 2600., 2700., 2800., 2900., 3000., + 3100., 3200., 3300., 3400., 3500., 3600., 3700., 3800., + 3900., 4000., 4100., 4200., 4300., 4400., 4500., 4600., + 4700., 4800., 4900., 5000., 5100., 5200., 5300., 5400., + 5500., 5600., 5700., 5800., 5900., 6000., 6100., 6200., + 6300., 6400., 6500., 6600., 6700., 6800., 6900., 7000., + 7200., 7400., 7600., 7800., 8000., 8200., 8400., 8600., + 8800., 9000., 9200., 9400., 9600., 9800., 10000., 10200., + 10400., 10600., 10800., 11000., 11200., 11400., 11600., 11800., + 12000. + ], dtype=float) + + DEFAULT_LOGG_GRID = np.array([0., 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6.], dtype=float) + DEFAULT_FEH_GRID = np.array([-3.0, -2.0, -1.5, -1.0, -0.5, 0.0, 0.5], dtype=float) + + def __init__(self, base_dir, + wave_filename="WAVE_PHOENIX-ACES-AGSS-COND-2011.fits", + model_tag="PHOENIX-ACES-AGSS-COND-2011-HiRes", + phoenix_wave_medium="vacuum", + verbose=True): + self._flux_grid = None + self.base_dir = os.path.abspath(os.path.expanduser(base_dir)) + self.wave_filename = wave_filename + self.model_tag = model_tag + self.phoenix_wave_medium = str(phoenix_wave_medium).lower() + self.verbose = bool(verbose) + + wave_path = os.path.join(self.base_dir, self.wave_filename) + if not os.path.exists(wave_path): + raise FileNotFoundError("PHOENIX wavelength file not found: {0}".format(wave_path)) + + self.phoenix_wave = fits.getdata(wave_path).astype(float) + self._interp = None + self._grid = None + self.wave = None # the wave grid of the interpolator, usually observed_wave + + def template_path(self, teff, logg, feh): + rel = phoenix_relpath(teff, logg, feh, model_tag=self.model_tag) + return os.path.join(self.base_dir, rel) + + def scan_available_points(self): + """ + Scan the local PHOENIX directory tree and return the available grid + points as a sorted list of (teff, feh, logg) tuples. + """ + import glob + import re + + pat = re.compile( + r"lte(?P\d+)-(?P\d+\.\d+)(?P[+-]\d+\.\d+)\.PHOENIX" + ) + + points = [] + for zdir in sorted(glob.glob(os.path.join(self.base_dir, "Z*"))): + for path in sorted(glob.glob(os.path.join(zdir, "lte*.fits"))): + name = os.path.basename(path) + m = pat.search(name) + if m is None: + continue + + teff = float(m.group("teff")) + logg = float(m.group("logg")) + feh = float(m.group("feh")) + points.append((teff, feh, logg)) + + if len(points) == 0: + raise RuntimeError( + "No PHOENIX templates found under {0}".format(self.base_dir) + ) + + return sorted(set(points)) + + def available_axes(self): + """ + Return the actually installed Teff / [Fe/H] / logg axes. + """ + pts = self.scan_available_points() + teff = np.unique([p[0] for p in pts]).astype(float) + feh = np.unique([p[1] for p in pts]).astype(float) + logg = np.unique([p[2] for p in pts]).astype(float) + return teff, feh, logg + + def has_template(self, teff, logg, feh): + """ + Return True if the requested PHOENIX template exists on disk. + """ + return os.path.exists(self.template_path(teff, logg, feh)) + + def complete_subgrid(self, teff_grid, feh_grid, logg_grid, max_iter=20): + """ + Trim candidate Teff / [Fe/H] / logg axes to a complete rectangular + subgrid that actually exists on disk. + + This is conservative: it iteratively removes axis values that do not + have templates for all combinations with the current remaining axes. + """ + teff_grid = np.unique(_as_float_array(teff_grid)) + feh_grid = np.unique(_as_float_array(feh_grid)) + logg_grid = np.unique(_as_float_array(logg_grid)) + + for _ in range(int(max_iter)): + changed = False + + teff_new = np.array([ + t for t in teff_grid + if all(self.has_template(t, g, z) for g in logg_grid for z in feh_grid) + ], dtype=float) + if len(teff_new) != len(teff_grid): + teff_grid = teff_new + changed = True + + feh_new = np.array([ + z for z in feh_grid + if all(self.has_template(t, g, z) for t in teff_grid for g in logg_grid) + ], dtype=float) + if len(feh_new) != len(feh_grid): + feh_grid = feh_new + changed = True + + logg_new = np.array([ + g for g in logg_grid + if all(self.has_template(t, g, z) for t in teff_grid for z in feh_grid) + ], dtype=float) + if len(logg_new) != len(logg_grid): + logg_grid = logg_new + changed = True + + if len(teff_grid) == 0 or len(feh_grid) == 0 or len(logg_grid) == 0: + raise ValueError("No complete PHOENIX subgrid remains after availability filtering.") + + if not changed: + break + else: + raise RuntimeError("complete_subgrid did not converge within max_iter.") + + return teff_grid, feh_grid, logg_grid + + def load_template(self, teff, logg, feh, wave=None, wave_medium=None): + """ + Load a single PHOENIX template flux array. + + If `wave` is provided, resample the template onto that wavelength grid. + The PHOENIX wavelength grid is first converted from `self.phoenix_wave_medium` + into `wave_medium` when needed. + """ + path = self.template_path(teff, logg, feh) + if not os.path.exists(path): + raise FileNotFoundError("PHOENIX template not found: {0}".format(path)) + + flux = fits.getdata(path).astype(float) + + template_wave = self.phoenix_wave.copy() + target_medium = self.phoenix_wave_medium if wave_medium is None else str(wave_medium).lower() + + if target_medium != self.phoenix_wave_medium: + template_wave = convert_wavelength_medium( + template_wave, + from_medium=self.phoenix_wave_medium, + to_medium=target_medium, + ) + + if wave is None: + return template_wave, flux + + wave = _as_float_array(wave) + + wmin, wmax = float(wave.min()), float(wave.max()) + mask = (template_wave >= wmin) & (template_wave <= wmax) + if mask.sum() < 4: + raise ValueError("Requested wave range does not overlap PHOENIX wave grid.") + + spline = InterpolatedUnivariateSpline(template_wave[mask], flux[mask], k=3, ext=1) + return wave, spline(wave) + + def _prepare_resampling_grid(self, observed_wave, observed_wave_medium): + """ + Precompute the PHOENIX wavelength grid transformed into the requested + observed wavelength medium, along with the overlap mask used for all + templates in this interpolator build. + """ + observed_wave = _as_float_array(observed_wave) + observed_wave_medium = ( + self.phoenix_wave_medium + if observed_wave_medium is None + else str(observed_wave_medium).lower() + ) + + template_wave = self.phoenix_wave + if observed_wave_medium != self.phoenix_wave_medium: + template_wave = convert_wavelength_medium( + template_wave, + from_medium=self.phoenix_wave_medium, + to_medium=observed_wave_medium, + ) + + wmin, wmax = float(observed_wave.min()), float(observed_wave.max()) + mask = (template_wave >= wmin) & (template_wave <= wmax) + if mask.sum() < 4: + raise ValueError("Requested wave range does not overlap PHOENIX wave grid.") + + return template_wave[mask], mask + + def _resample_template_fast(self, path, wave_clip, mask, observed_wave): + """ + Fast path for build_interpolator(): read flux, apply precomputed overlap + mask, and resample onto observed_wave without repeating wavelength-medium + conversion or overlap calculations for every template. + """ + flux = fits.getdata(path).astype(float)[mask] + spline = InterpolatedUnivariateSpline(wave_clip, flux, k=3, ext=1) + return spline(observed_wave) + + @staticmethod + def _default_cache_name(teff_grid, feh_grid, logg_grid, wave): + h = hashlib.md5() + h.update(np.asarray(teff_grid, dtype=float).tobytes()) + h.update(np.asarray(feh_grid, dtype=float).tobytes()) + h.update(np.asarray(logg_grid, dtype=float).tobytes()) + h.update(np.asarray(wave, dtype=float).tobytes()) + return "phoenix_cache_{0}.npz".format(h.hexdigest()[:12]) + + def build_interpolator(self, + observed_wave, + teff_grid=None, + feh_grid=None, + logg_grid=None, + observed_wave_medium=None, + cache_path=None, + allow_missing=False): + """ + Build or load a RegularGridInterpolator on (Teff, [Fe/H], logg). + + The interpolator is defined on `observed_wave`, i.e. an observed-grid + backend. For performance, the PHOENIX wavelength grid is transformed into + the requested wavelength medium and clipped to the observed wavelength + range once per build, then each template flux array is resampled using + that precomputed support. + + If `cache_path` exists and matches the requested wavelength grid, parameter + axes, and wavelength-medium metadata, the cached flux cube is loaded. + Otherwise the cube is rebuilt and optionally written back to disk. + + Parameters + ---------- + observed_wave : array-like + Target wavelength grid for the interpolator. + teff_grid, feh_grid, logg_grid : array-like or None + Parameter axes for the PHOENIX cube. If None, use the default axes. + observed_wave_medium : {"air", "vacuum"} or None + Wavelength medium of `observed_wave`. If None, assume the PHOENIX + native wavelength medium. + cache_path : str or None + Optional path to an on-disk cache of the resampled flux cube. + allow_missing : bool + If True, missing templates are skipped and left as NaN in the flux cube. + If False, missing templates raise immediately. + + Returns + ------- + scipy.interpolate.RegularGridInterpolator + Interpolator returning flux on `observed_wave`. + """ + observed_wave = _as_float_array(observed_wave) + observed_wave_medium = ( + self.phoenix_wave_medium + if observed_wave_medium is None + else str(observed_wave_medium).lower() + ) + self.wave = observed_wave.copy() + + teff_grid = self.DEFAULT_TEFF_GRID if teff_grid is None else _as_float_array(teff_grid) + feh_grid = self.DEFAULT_FEH_GRID if feh_grid is None else _as_float_array(feh_grid) + logg_grid = self.DEFAULT_LOGG_GRID if logg_grid is None else _as_float_array(logg_grid) + + if cache_path is not None: + cache_path = os.path.abspath(os.path.expanduser(cache_path)) + if os.path.exists(cache_path): + try: + self.load_cache( + cache_path, + expected_wave=observed_wave, + expected_teff_grid=teff_grid, + expected_feh_grid=feh_grid, + expected_logg_grid=logg_grid, + expected_observed_wave_medium=observed_wave_medium, + ) + return self._interp + except ValueError as e: + if self.verbose: + print("Cache mismatch, rebuilding:", cache_path) + print(str(e)) + + # Precompute the PHOENIX support grid in the requested wavelength medium + # and its overlap with the observed wavelength grid once, outside the + # template loop. + wave_clip, mask = self._prepare_resampling_grid( + observed_wave=observed_wave, + observed_wave_medium=observed_wave_medium, + ) + + flux_grid = np.full( + (len(teff_grid), len(feh_grid), len(logg_grid), len(observed_wave)), + np.nan, + dtype=float, + ) + + # Fast observed-grid build: read only the template flux array for each + # grid point, then resample using the precomputed wavelength support. + for it, teff in enumerate(teff_grid): + for iz, feh in enumerate(feh_grid): + for ig, logg in enumerate(logg_grid): + try: + path = self.template_path(teff, logg, feh) + if not os.path.exists(path): + raise FileNotFoundError("PHOENIX template not found: {0}".format(path)) + + f = self._resample_template_fast( + path=path, + wave_clip=wave_clip, + mask=mask, + observed_wave=observed_wave, + ) + flux_grid[it, iz, ig, :] = f + except Exception as e: + if not allow_missing: + raise + if self.verbose: + print( + "Skipping missing template teff={0} feh={1} logg={2}: {3}".format( + teff, feh, logg, str(e) + ) + ) + + self._flux_grid = flux_grid + + if np.isnan(flux_grid).any() and not allow_missing: + raise RuntimeError("NaNs present in flux_grid but allow_missing=False.") + + # RegularGridInterpolator expects axes order matching the data + self._grid = (teff_grid, feh_grid, logg_grid) + self._interp = RegularGridInterpolator(self._grid, flux_grid, method="linear", bounds_error=True) + + if cache_path is not None: + self.save_cache(cache_path, observed_wave_medium=observed_wave_medium) + + return self._interp + + def evaluate(self, teff, feh, logg): + """ + Evaluate the interpolated spectrum on self.wave. + build_interpolator() must have been called first. + """ + if self._interp is None or self.wave is None: + raise RuntimeError("Interpolator not built. Call build_interpolator() first.") + p = (float(teff), float(feh), float(logg)) + return self._interp(p) + + def save_cache(self, cache_path, observed_wave_medium): + if self._grid is None or self.wave is None or self._flux_grid is None: + raise RuntimeError("Nothing to save. Build interpolator first.") + + teff_grid, feh_grid, logg_grid = self._grid + flux = self._flux_grid.astype(float, copy=False) + + np.savez_compressed( + cache_path, + teff_grid=np.asarray(teff_grid, dtype=float), + feh_grid=np.asarray(feh_grid, dtype=float), + logg_grid=np.asarray(logg_grid, dtype=float), + wave=np.asarray(self.wave, dtype=float), + flux_grid=flux, + model_tag=self.model_tag, + wave_filename=self.wave_filename, + phoenix_wave_medium=np.asarray(self.phoenix_wave_medium), + observed_wave_medium=np.asarray(observed_wave_medium), + ) + if self.verbose: + print("Saved PHOENIX cache to {0}".format(cache_path)) + + def load_cache( + self, + cache_path, + expected_wave=None, + expected_teff_grid=None, + expected_feh_grid=None, + expected_logg_grid=None, + expected_observed_wave_medium=None, + ): + d = np.load(cache_path, allow_pickle=False) + + teff_grid = d["teff_grid"].astype(float) + feh_grid = d["feh_grid"].astype(float) + logg_grid = d["logg_grid"].astype(float) + wave = d["wave"].astype(float) + flux_grid = d["flux_grid"] + cached_model_tag = _normalize_cache_string(d["model_tag"]) if "model_tag" in d.files else None + cached_wave_filename = _normalize_cache_string(d["wave_filename"]) if "wave_filename" in d.files else None + cached_phoenix_wave_medium = _normalize_cache_string(d["phoenix_wave_medium"]) if "phoenix_wave_medium" in d.files else None + cached_observed_wave_medium = _normalize_cache_string(d["observed_wave_medium"]) if "observed_wave_medium" in d.files else None + + if expected_wave is not None: + if not _same_float_array(expected_wave, wave): + raise ValueError("Cached wavelength grid does not match requested observed_wave.") + + if expected_teff_grid is not None: + if not _same_float_array(expected_teff_grid, teff_grid): + raise ValueError("Cached teff_grid does not match requested teff_grid.") + + if expected_feh_grid is not None: + if not _same_float_array(expected_feh_grid, feh_grid): + raise ValueError("Cached feh_grid does not match requested feh_grid.") + + if expected_logg_grid is not None: + if not _same_float_array(expected_logg_grid, logg_grid): + raise ValueError("Cached logg_grid does not match requested logg_grid.") + + if cached_model_tag is None: + raise ValueError("Cached model_tag is missing.") + + if cached_model_tag != str(self.model_tag): + raise ValueError( + "Cached model_tag does not match current PhoenixLibrary.model_tag: " + "{0} != {1}".format(cached_model_tag, self.model_tag) + ) + + if cached_wave_filename is None: + raise ValueError("Cached wave_filename is missing.") + + if cached_wave_filename != str(self.wave_filename): + raise ValueError( + "Cached wave_filename does not match current PhoenixLibrary.wave_filename: " + "{0} != {1}".format(cached_wave_filename, self.wave_filename) + ) + + if cached_phoenix_wave_medium is None: + raise ValueError("Cached phoenix_wave_medium is missing.") + if cached_phoenix_wave_medium != str(self.phoenix_wave_medium): + raise ValueError( + "Cached phoenix_wave_medium does not match current PhoenixLibrary.phoenix_wave_medium: " + "{0} != {1}".format(cached_phoenix_wave_medium, self.phoenix_wave_medium) + ) + + if expected_observed_wave_medium is not None: + if cached_observed_wave_medium is None: + raise ValueError("Cached observed_wave_medium is missing.") + if cached_observed_wave_medium != str(expected_observed_wave_medium): + raise ValueError( + "Cached observed_wave_medium does not match requested observed_wave_medium: " + "{0} != {1}".format(cached_observed_wave_medium, expected_observed_wave_medium) + ) + + self.wave = wave + self._grid = (teff_grid, feh_grid, logg_grid) + self._flux_grid = flux_grid.astype(float, copy=False) + + self._interp = RegularGridInterpolator( + self._grid, + self._flux_grid, + method="linear", + bounds_error=True, + ) + + if self.verbose: + print("Loaded PHOENIX cache from {0}".format(cache_path)) + + return self._interp diff --git a/Spyctres/phoenix_forward.py b/Spyctres/phoenix_forward.py new file mode 100644 index 0000000..34e2e2f --- /dev/null +++ b/Spyctres/phoenix_forward.py @@ -0,0 +1,459 @@ +# Spyctres/phoenix_forward.py +import numpy as np +from scipy.interpolate import interp1d +from scipy.ndimage import gaussian_filter1d + +from .waveutils import convert_wavelength_medium, C_KMS + +def _coerce_segment_list(segments): + """ + Normalize a segment-like input to a non-empty plain list. + + Accepted inputs + --------------- + - a single segment-like object with a ``wave`` attribute + - a list/tuple of such objects + - a collection-like object with a ``segments`` attribute + """ + if hasattr(segments, "segments"): + seg_list = list(getattr(segments, "segments")) + elif isinstance(segments, (list, tuple)): + seg_list = list(segments) + else: + seg_list = [segments] + + if len(seg_list) == 0: + raise ValueError("No segments were provided.") + + for i, seg in enumerate(seg_list): + if not hasattr(seg, "wave"): + raise TypeError("Item {0} does not look like a spectrum segment.".format(i)) + + return seg_list + +def _normalize_wave_medium(wave_medium, default="unknown"): + if wave_medium is None: + return str(default).lower() + s = str(wave_medium).strip().lower() + if s in ("air", "vacuum", "unknown"): + return s + return str(default).lower() + + +def infer_segments_wave_medium(segments, default="unknown"): + """ + Infer a common wavelength medium from a segment list or collection. + + Returns + ------- + str + "air" or "vacuum" if all segments agree on a recognized medium, + otherwise ``default`` normalized through _normalize_wave_medium(). + """ + segments = _coerce_segment_list(segments) + media = sorted(set(_normalize_wave_medium(seg.wave_medium, default=default) for seg in segments)) + if len(media) == 1 and media[0] in ("air", "vacuum"): + return media[0] + return _normalize_wave_medium(default, default=default) + + +def fit_bounds_from_segments(segments, use_fit_mask=True): + """ + Return the min/max wavelength bounds that should define the model support. + + Parameters + ---------- + segments : segment list or collection + use_fit_mask : bool, default=True + If True, only seg.mask-selected pixels contribute to the bounds. + + Returns + ------- + (wmin, wmax) : tuple[float, float] + """ + segments = _coerce_segment_list(segments) + + los = [] + his = [] + + for seg in segments: + wave = np.asarray(seg.wave, dtype=float) + + if use_fit_mask: + m = np.asarray(seg.mask, dtype=bool) + else: + m = np.isfinite(wave) + + m &= np.isfinite(wave) + if np.any(m): + los.append(float(np.min(wave[m]))) + his.append(float(np.max(wave[m]))) + + if len(los) == 0: + raise ValueError("No valid segment wavelengths found for model bounds.") + + return min(los), max(his) + + +def build_native_interp_wave_grid_for_segments( + segments, + phoenix_lib, + model_margin_A=200.0, +): + """ + Build the native PHOENIX wavelength grid needed for native_interp modeling. + + The returned grid is a clipped subset of phoenix_lib.phoenix_wave, converted + into the common wavelength medium of the supplied segments and restricted + to the fit-mask bounds plus a margin. + + This helper deliberately uses phoenix_lib.phoenix_wave, not phoenix_lib.wave. + phoenix_lib.wave is the current interpolator grid and may already be an + observed or cached model grid. + """ + segments = _coerce_segment_list(segments) + + if getattr(phoenix_lib, "phoenix_wave", None) is None: + raise ValueError("phoenix_lib.phoenix_wave is not initialized.") + + model_wave_medium = infer_segments_wave_medium( + segments, + default=getattr(phoenix_lib, "phoenix_wave_medium", "vacuum"), + ) + + fit_min, fit_max = fit_bounds_from_segments( + segments, + use_fit_mask=True, + ) + + phoenix_wave = np.asarray(phoenix_lib.phoenix_wave, dtype=float) + + model_wave_grid, _dummy_flux = prepare_phoenix_native_template( + phoenix_wave_native=phoenix_wave, + template_flux_native=np.ones_like(phoenix_wave, dtype=float), + target_wave_medium=model_wave_medium, + phoenix_wave_medium=getattr(phoenix_lib, "phoenix_wave_medium", "vacuum"), + wmin=float(fit_min), + wmax=float(fit_max), + margin_A=float(model_margin_A), + ) + + return model_wave_grid, model_wave_medium + + +def prepare_phoenix_native_template( + phoenix_wave_native, + template_flux_native, + target_wave_medium, + phoenix_wave_medium="vacuum", + wmin=None, + wmax=None, + margin_A=200.0, +): + """ + Prepare a native PHOENIX template before RV shift and convolution. + + Steps: + 1. Convert PHOENIX wavelengths into the target data wavelength medium. + 2. Subset to [wmin - margin_A, wmax + margin_A] if bounds are given. + 3. Return sorted finite arrays. + """ + wave = np.asarray(phoenix_wave_native, dtype=np.float64).copy() + flux = np.asarray(template_flux_native, dtype=np.float64).copy() + + src_medium = _normalize_wave_medium(phoenix_wave_medium, default="vacuum") + dst_medium = _normalize_wave_medium(target_wave_medium, default=src_medium) + + if src_medium in ("air", "vacuum") and dst_medium in ("air", "vacuum") and src_medium != dst_medium: + wave = convert_wavelength_medium( + wave, + from_medium=src_medium, + to_medium=dst_medium, + ) + + m = np.isfinite(wave) & np.isfinite(flux) & (wave > 0) + + if wmin is not None: + m &= (wave >= float(wmin) - float(margin_A)) + if wmax is not None: + m &= (wave <= float(wmax) + float(margin_A)) + + wave = wave[m] + flux = flux[m] + + if len(wave) < 10: + raise ValueError("Too few PHOENIX points remain after native-template preparation.") + + if not np.all(np.diff(wave) > 0): + idx = np.argsort(wave) + wave = wave[idx] + flux = flux[idx] + + return wave, flux + + +def doppler_shift_wave(wave_A, rv_kms): + """ + Apply a non-relativistic Doppler shift to a wavelength array. + + This matches the Gaia21ccu notebook-faithful reference path currently used for + X-SHOOTER development. + """ + wave_A = np.asarray(wave_A, dtype=np.float64) + return wave_A * (1.0 + float(rv_kms) / C_KMS) + + +def convolve_to_resolution_loglam(wave_A, flux, R=None, fwhm_kms=None): + """ + Convolve a spectrum with a Gaussian LSF on a log-lambda grid. + + The returned array always has the same shape and ordering as the input flux. + Exactly one of ``R`` or ``fwhm_kms`` may be supplied. If both are None, + the input flux is returned unchanged. + """ + wave_A = np.asarray(wave_A, dtype=np.float64) + flux = np.asarray(flux, dtype=np.float64) + + if wave_A.shape != flux.shape: + raise ValueError("wave_A and flux must have the same shape.") + + if (R is not None) and (fwhm_kms is not None): + raise ValueError("Provide only one of R or fwhm_kms, not both.") + + if fwhm_kms is None and R is None: + return flux.copy() + + if fwhm_kms is None: + R = float(R) + if R <= 0: + raise ValueError("R must be > 0.") + fwhm_kms = C_KMS / R + else: + fwhm_kms = float(fwhm_kms) + if fwhm_kms <= 0: + raise ValueError("fwhm_kms must be > 0.") + + good = np.isfinite(wave_A) & (wave_A > 0) & np.isfinite(flux) + if np.sum(good) < 5: + return flux.copy() + + w_good = wave_A[good] + f_good = flux[good] + + order = np.argsort(w_good) + w_sorted = w_good[order] + f_sorted = f_good[order] + + loglam = np.log(w_sorted) + dloglam = np.nanmedian(np.diff(loglam)) + if (not np.isfinite(dloglam)) or (dloglam <= 0): + return flux.copy() + + sigma_v = fwhm_kms / 2.3548200450309493 + sigma_pix = (sigma_v / C_KMS) / dloglam + + if (not np.isfinite(sigma_pix)) or (sigma_pix < 0.3): + return flux.copy() + + f_conv_sorted = gaussian_filter1d(f_sorted, sigma_pix, mode="nearest") + + f_conv_good = np.empty_like(f_good) + f_conv_good[order] = f_conv_sorted + + out = flux.copy() + out[good] = f_conv_good + return out + + +def resample_flux(w_src, f_src, w_tgt, extrapolate=True): + """ + Resample a model spectrum onto a target wavelength grid. + + The default uses linear extrapolation to match the validated notebook-scan + reference. In normal use, a sufficient margin_A should make extrapolation + unnecessary on fitted pixels. + """ + w_src = np.asarray(w_src, dtype=np.float64) + f_src = np.asarray(f_src, dtype=np.float64) + w_tgt = np.asarray(w_tgt, dtype=np.float64) + + m = np.isfinite(w_src) & np.isfinite(f_src) + w_src = w_src[m] + f_src = f_src[m] + + if len(w_src) < 4: + return np.full_like(w_tgt, np.nan, dtype=float) + + if not np.all(np.diff(w_src) > 0): + idx = np.argsort(w_src) + w_src = w_src[idx] + f_src = f_src[idx] + + fill_value = "extrapolate" if extrapolate else np.nan + f = interp1d( + w_src, + f_src, + kind="linear", + bounds_error=False, + fill_value=fill_value, + ) + return np.asarray(f(w_tgt), dtype=float) + + +def build_phoenix_native_model_to_wave( + wave_target, + phoenix_wave_native, + template_flux_native, + rv_kms=0.0, + rv_bary_kms=0.0, + R=None, + fwhm_kms=None, + target_wave_medium="vacuum", + phoenix_wave_medium="vacuum", + wmin=None, + wmax=None, + model_margin_A=200.0, + extrapolate=True, +): + """ + Build a PHOENIX model on a target observed wavelength grid using the + native-grid forward-model order: + + convert medium -> subset with margin -> Doppler shift -> convolve -> + resample to target grid + """ + w_native, f_native = prepare_phoenix_native_template( + phoenix_wave_native=phoenix_wave_native, + template_flux_native=template_flux_native, + target_wave_medium=target_wave_medium, + phoenix_wave_medium=phoenix_wave_medium, + wmin=wmin, + wmax=wmax, + margin_A=model_margin_A, + ) + + rv_tot = float(rv_bary_kms) + float(rv_kms) + w_shift = doppler_shift_wave(w_native, rv_tot) + + m = np.isfinite(w_shift) & (w_shift > 0) & np.isfinite(f_native) + w_shift = w_shift[m] + f_native = f_native[m] + + f_conv = convolve_to_resolution_loglam(w_shift, f_native, R=R, fwhm_kms=fwhm_kms) + + return resample_flux( + w_src=w_shift, + f_src=f_conv, + w_tgt=wave_target, + extrapolate=extrapolate, + ) + + +def build_phoenix_native_models_for_segments( + segments, + phoenix_wave_native, + template_flux_native, + rv_kms=0.0, + rv_bary_kms=0.0, + R=None, + fwhm_kms=None, + segment_fwhm_kms=None, + phoenix_wave_medium="vacuum", + model_margin_A=200.0, + bounds_use_fit_mask=True, + extrapolate=True, + return_native=False, +): + """ + Build one PHOENIX model array per segment using the native-grid + forward-model order. + + This function is continuum-agnostic. It returns only the physical template + prediction on each segment grid. Continuum nuisance terms should be handled + by higher-level fitting code. + + Parameters + ---------- + segments : segment list or collection + R, fwhm_kms : float, optional + Global instrumental broadening specification. Exactly one may be + supplied. Ignored if ``segment_fwhm_kms`` is provided. + segment_fwhm_kms : sequence, optional + Per-segment Gaussian LSF FWHM values in km/s. If supplied, must match + the number of kept segments. + """ + segments = _coerce_segment_list(segments) + + target_wave_medium = infer_segments_wave_medium( + segments, + default=phoenix_wave_medium, + ) + + wmin, wmax = fit_bounds_from_segments( + segments, + use_fit_mask=bounds_use_fit_mask, + ) + + w_native, f_native = prepare_phoenix_native_template( + phoenix_wave_native=phoenix_wave_native, + template_flux_native=template_flux_native, + target_wave_medium=target_wave_medium, + phoenix_wave_medium=phoenix_wave_medium, + wmin=wmin, + wmax=wmax, + margin_A=model_margin_A, + ) + + rv_tot = float(rv_bary_kms) + float(rv_kms) + w_shift = doppler_shift_wave(w_native, rv_tot) + + m = np.isfinite(w_shift) & (w_shift > 0) & np.isfinite(f_native) + w_shift = w_shift[m] + f_native = f_native[m] + + if segment_fwhm_kms is None: + if (R is not None) and (fwhm_kms is not None): + raise ValueError("Provide only one of R or fwhm_kms, not both.") + if fwhm_kms is None and R is None: + segment_fwhm_kms = [None] * len(segments) + elif fwhm_kms is not None: + segment_fwhm_kms = [float(fwhm_kms)] * len(segments) + else: + segment_fwhm_kms = [C_KMS / float(R)] * len(segments) + else: + if len(segment_fwhm_kms) != len(segments): + raise ValueError("segment_fwhm_kms must match the number of segments.") + segment_fwhm_kms = [None if x is None else float(x) for x in segment_fwhm_kms] + + conv_cache = {} + model_list = [] + + for seg, seg_fwhm in zip(segments, segment_fwhm_kms): + key = None if seg_fwhm is None else float(seg_fwhm) + if key not in conv_cache: + conv_cache[key] = convolve_to_resolution_loglam( + w_shift, + f_native, + fwhm_kms=key, + ) + + model_list.append( + resample_flux( + w_src=w_shift, + f_src=conv_cache[key], + w_tgt=np.asarray(seg.wave, dtype=float), + extrapolate=extrapolate, + ) + ) + + if not return_native: + return model_list + + return model_list, { + "target_wave_medium": target_wave_medium, + "wmin_fit": float(wmin), + "wmax_fit": float(wmax), + "wave_native_prepared": w_native, + "wave_shifted": w_shift, + "segment_fwhm_kms": segment_fwhm_kms, + } diff --git a/Spyctres/plotting.py b/Spyctres/plotting.py new file mode 100644 index 0000000..cbf0bb5 --- /dev/null +++ b/Spyctres/plotting.py @@ -0,0 +1,695 @@ +""" +Plotting utilities for Spyctres spectral diagnostics. + +This module provides lightweight, reusable plotting helpers for: +- marking common spectral lines +- plotting full-spectrum data/model comparisons with residuals +- plotting zoomed windows around selected line centers +- plotting binned spectrum overviews for one or more segments + +Design principles +----------------- +- use the Matplotlib object-oriented API +- do not call plt.show() internally +- return (fig, axes) so scripts and notebooks control display/saving +- accept plain arrays or simple spectrum containers, not instrument-specific logic +""" + +import numpy as np +import matplotlib.pyplot as plt + + +COMMON_LINES = { + "balmer": [ + ("Hδ", 4101.74), + ("Hγ", 4340.47), + ("Hβ", 4861.33), + ("Hα", 6562.80), + ], + "paschen": [ + ("Paγ", 10938.09), + ("Paβ", 12818.08), + ], + "brackett": [ + ("Brγ", 21661.0), + ], + "caii": [ + ("Ca II K", 3933.66), + ("Ca II H", 3968.47), + ("Ca II", 8498.02), + ("Ca II", 8542.09), + ("Ca II", 8662.14), + ], + "nai": [ + ("Na I D1", 5895.92), + ("Na I D2", 5889.95), + ], + "hei": [ + ("He I", 4471.48), + ("He I", 5875.62), + ("He I", 6678.15), + ], +} + + +def _as_float_array(x): + """Return x as a NumPy float array.""" + return np.asarray(x, dtype=float) + + +def _as_bool_array(x, n_expected=None): + """Return x as a NumPy boolean array.""" + arr = np.asarray(x, dtype=bool) + if n_expected is not None and arr.shape != (n_expected,): + raise ValueError("Boolean mask has wrong shape.") + return arr + + +def _compute_robust_ylim(y, lower=1.0, upper=99.0, pad_frac=0.05): + """ + Compute robust y-limits from percentiles. + + Parameters + ---------- + y : array-like + Input data. + lower, upper : float, optional + Percentiles used for the robust interval. + pad_frac : float, optional + Fractional padding applied to the interval. + + Returns + ------- + (ymin, ymax) : tuple of float + """ + y = _as_float_array(y) + y = y[np.isfinite(y)] + if y.size == 0: + return 0.0, 1.0 + + lo, hi = np.nanpercentile(y, [lower, upper]) + if not np.isfinite(lo) or not np.isfinite(hi) or hi <= lo: + lo = np.nanmin(y) + hi = np.nanmax(y) + if not np.isfinite(lo) or not np.isfinite(hi) or hi <= lo: + return 0.0, 1.0 + + pad = pad_frac * (hi - lo) + return lo - pad, hi + pad + + +def _mask_to_spans(wave, mask): + """ + Convert a boolean mask into contiguous wavelength spans. + + Parameters + ---------- + wave : array-like + Wavelength array. + mask : array-like of bool + Boolean mask on the same grid. + + Returns + ------- + spans : list of tuple + List of (wmin, wmax) spans for contiguous True regions. + """ + wave = _as_float_array(wave) + mask = _as_bool_array(mask, n_expected=wave.size) + + if wave.size == 0 or not mask.any(): + return [] + + idx = np.flatnonzero(mask) + groups = np.split(idx, np.where(np.diff(idx) > 1)[0] + 1) + + spans = [] + for g in groups: + if g.size == 0: + continue + spans.append((wave[g[0]], wave[g[-1]])) + return spans + + +def _resolve_line_group(group): + """ + Resolve a line-group specification into a list of (label, wavelength_A). + + Parameters + ---------- + group : str or list + Either a key in COMMON_LINES or a list of (label, wavelength_A) tuples. + + Returns + ------- + list + List of (label, wavelength_A) tuples. + """ + if isinstance(group, str): + return COMMON_LINES.get(group.lower(), []) + return group + + +def _extract_spectrum_arrays(spec): + """ + Extract wave/flux/mask/label from a simple spectrum container. + + Supported inputs: + - SpectrumSegment-like object with .wave, .flux, optional .mask, optional .name + - dict with keys 'wave', 'flux', optional 'mask', optional 'label' or 'name' + + Parameters + ---------- + spec : object + Spectrum container. + + Returns + ------- + wave, flux, mask, label : tuple + """ + if isinstance(spec, dict): + wave = _as_float_array(spec["wave"]) + flux = _as_float_array(spec["flux"]) + mask = spec.get("mask", None) + label = spec.get("label", spec.get("name", None)) + else: + wave = _as_float_array(spec.wave) + flux = _as_float_array(spec.flux) + mask = getattr(spec, "mask", None) + label = getattr(spec, "name", None) + + if mask is None: + mask = np.isfinite(wave) & np.isfinite(flux) + else: + mask = _as_bool_array(mask, n_expected=wave.size) + + return wave, flux, mask, label + + +def mark_lines(ax, lines, xlim=None, ymin=0.0, ymax=0.98, alpha=0.25, fontsize=7): + """ + Draw vertical markers for known spectral lines. + + Parameters + ---------- + ax : matplotlib.axes.Axes + Axes on which to draw. + lines : list of (label, wavelength_A) + Spectral-line labels and wavelengths in Angstrom. + xlim : tuple, optional + Plot only lines within this wavelength range. If None, uses current x-limits. + ymin, ymax : float, optional + Vertical span of the markers in axes coordinates. + alpha : float, optional + Line/text transparency. + fontsize : float, optional + Text size for labels. + """ + if xlim is None: + xlim = ax.get_xlim() + + for label, lam in lines: + if xlim[0] <= lam <= xlim[1]: + ax.axvline(lam, ls="--", lw=1.0, alpha=alpha) + ax.text( + lam, + ymax, + label, + rotation=90, + va="top", + ha="right", + fontsize=fontsize, + alpha=alpha, + transform=ax.get_xaxis_transform(), + ) + + +def bin_median_spectrum(wave, flux, good=None, nbins=600): + """ + Bin a spectrum using the median flux in each wavelength bin. + + Parameters + ---------- + wave, flux : array-like + Input wavelength and flux arrays. + good : array-like of bool, optional + Valid-data mask. If None, finite wave/flux points are used. + nbins : int, optional + Number of wavelength bins. + + Returns + ------- + wmid, fb : ndarray + Bin-center wavelengths and binned median fluxes. + """ + wave = _as_float_array(wave) + flux = _as_float_array(flux) + + if wave.shape != flux.shape: + raise ValueError("wave and flux must have the same shape.") + + if good is None: + good = np.isfinite(wave) & np.isfinite(flux) + else: + good = _as_bool_array(good, n_expected=wave.size) + + ww = wave[good] + ff = flux[good] + + if ww.size == 0: + return np.array([]), np.array([]) + + idx = np.argsort(ww) + ww = ww[idx] + ff = ff[idx] + + bins = np.linspace(ww.min(), ww.max(), int(nbins) + 1) + wmid = 0.5 * (bins[:-1] + bins[1:]) + fb = np.full(int(nbins), np.nan) + + for i in range(int(nbins)): + m = (ww >= bins[i]) & (ww < bins[i + 1]) + if np.any(m): + fb[i] = np.nanmedian(ff[m]) + + ok = np.isfinite(fb) + return wmid[ok], fb[ok] + + +def plot_binned_spectra( + spectra, + nbins=600, + title=None, + figsize=(7, 3), + xlabel="Wavelength (Å)", + ylabel="Flux (binned)", + highlight_regions=None, +): + """ + Plot one or more spectra after median binning. + + Parameters + ---------- + spectra : list + List of spectrum containers. Each element may be: + - a SpectrumSegment-like object with .wave, .flux, optional .mask, optional .name + - a dict with keys 'wave', 'flux', optional 'mask', optional 'label' + nbins : int, optional + Number of bins per spectrum. + title : str, optional + Figure title. + figsize : tuple, optional + Figure size in inches. + xlabel, ylabel : str, optional + Axis labels. + highlight_regions : list of (wmin, wmax), optional + Wavelength spans to highlight with shaded regions. + + Returns + ------- + fig : matplotlib.figure.Figure + ax : matplotlib.axes.Axes + """ + fig, ax = plt.subplots(figsize=figsize) + + for spec in spectra: + wave, flux, mask, label = _extract_spectrum_arrays(spec) + wb, fb = bin_median_spectrum(wave, flux, good=mask, nbins=nbins) + if wb.size == 0: + continue + ax.plot(wb, fb, lw=1.0, label=label) + + if highlight_regions is not None: + for wmin, wmax in highlight_regions: + ax.axvspan(wmin, wmax, alpha=0.15) + + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + if title is not None: + ax.set_title(title) + + handles, labels = ax.get_legend_handles_labels() + if labels: + ax.legend(frameon=False, loc="best") + + fig.tight_layout() + return fig, ax + + +def plot_full_spectrum_fit( + wave, + flux, + err=None, + model=None, + used_mask=None, + excluded_mask=None, + title=None, + line_groups=None, + figsize=(10, 6), + flux_label="Flux", + data_label="Data", + model_label="Model", + residual_ylim=(-6, 6), + shade_excluded=True, +): + """ + Plot a full-spectrum fit with residuals. + + Parameters + ---------- + wave, flux : array-like + Observed wavelength and flux arrays. + err : array-like or None, optional + 1-sigma uncertainties. If provided with `model`, normalized residuals + `(flux - model) / err` are plotted. Otherwise raw residuals are shown. + model : array-like, optional + Model flux on the same wavelength grid. + used_mask : array-like of bool, optional + Mask of points used in the fit. If provided, points not used can be + highlighted in the top panel. + excluded_mask : array-like of bool, optional + Additional exclusion mask. Typically this marks tellurics or + user-excluded regions. + title : str, optional + Figure title. + line_groups : list, optional + List of line groups to mark. Each element may be: + - a key in COMMON_LINES, e.g. "balmer" + - a list of (label, wavelength_A) + figsize : tuple, optional + Figure size in inches. + flux_label : str, optional + Y-axis label for the top panel. + data_label, model_label : str, optional + Legend labels for data and model. + residual_ylim : tuple, optional + Y-limits for normalized residuals. + shade_excluded : bool, optional + If True, excluded regions are shaded. Otherwise excluded points are + overplotted as markers. + + Returns + ------- + fig : matplotlib.figure.Figure + axes : tuple + (ax_flux, ax_resid) + """ + wave = _as_float_array(wave) + flux = _as_float_array(flux) + + if wave.shape != flux.shape: + raise ValueError("wave and flux must have the same shape.") + + if err is not None: + err = _as_float_array(err) + if err.shape != wave.shape: + raise ValueError("err must match wave shape.") + + if model is not None: + model = _as_float_array(model) + if model.shape != wave.shape: + raise ValueError("model must match wave shape.") + + if used_mask is None: + used_mask = np.ones_like(wave, dtype=bool) + else: + used_mask = _as_bool_array(used_mask, n_expected=wave.size) + + if excluded_mask is not None: + excluded_mask = _as_bool_array(excluded_mask, n_expected=wave.size) + + fig, (ax_flux, ax_resid) = plt.subplots( + 2, + 1, + figsize=figsize, + sharex=True, + gridspec_kw={"height_ratios": [3, 1]}, + ) + + ax_flux.plot(wave, flux, lw=0.8, label=data_label) + if model is not None: + ax_flux.plot(wave, model, lw=0.8, label=model_label) + + if (~used_mask).any(): + ax_flux.plot( + wave[~used_mask], + flux[~used_mask], + ".", + ms=2, + alpha=0.5, + label="Unused", + ) + + if excluded_mask is not None and excluded_mask.any(): + if shade_excluded: + for wmin, wmax in _mask_to_spans(wave, excluded_mask): + ax_flux.axvspan(wmin, wmax, alpha=0.12) + ax_resid.axvspan(wmin, wmax, alpha=0.12) + else: + ax_flux.plot( + wave[excluded_mask], + flux[excluded_mask], + ".", + ms=2, + alpha=0.5, + label="Excluded", + ) + + ylo, yhi = _compute_robust_ylim(flux[used_mask] if used_mask.any() else flux) + ax_flux.set_ylim(ylo, yhi) + ax_flux.set_ylabel(flux_label) + + handles, labels = ax_flux.get_legend_handles_labels() + if labels: + ax_flux.legend(frameon=False, loc="best") + + if title is not None: + ax_flux.set_title(title) + + if model is not None: + resid = flux - model + ax_resid.axhline(0.0, lw=1.0, alpha=0.6) + + if err is not None: + good = np.isfinite(err) & (err > 0) + resid_plot = np.full_like(resid, np.nan, dtype=float) + resid_plot[good] = resid[good] / err[good] + ax_resid.plot(wave, resid_plot, lw=0.6) + ax_resid.set_ylabel("(D-M)/σ") + ax_resid.set_ylim(*residual_ylim) + else: + ax_resid.plot(wave, resid, lw=0.6) + ax_resid.set_ylabel("D-M") + else: + ax_resid.axis("off") + + if line_groups is not None: + xlim = (wave.min(), wave.max()) + for group in line_groups: + lines = _resolve_line_group(group) + mark_lines(ax_flux, lines, xlim=xlim, ymax=0.98) + + ax_resid.set_xlabel("Wavelength (Å)") + fig.tight_layout() + return fig, (ax_flux, ax_resid) + + +def plot_fit_windows( + wave, + flux, + err, + model, + windows, + ncols=2, + title=None, + line_groups=None, + excluded_mask=None, + figsize_per_panel=(5, 3), + data_label="Data", + model_label="Model", +): + """ + Plot zoomed windows comparing data and model. + + Parameters + ---------- + wave, flux : array-like + Observed wavelength and flux arrays. + err : array-like or None + Uncertainty array. Currently retained for interface consistency. + model : array-like + Model flux on the same wavelength grid. + windows : list + Window specification. Each element may be: + - (label, wmin, wmax) + - (wmin, wmax) + ncols : int, optional + Number of columns in the panel grid. + title : str, optional + Figure title. + line_groups : list, optional + Optional spectral-line groups to mark. + excluded_mask : array-like of bool, optional + Regions to shade in the zoomed panels. + figsize_per_panel : tuple, optional + Figure size per panel in inches. + data_label, model_label : str, optional + Legend labels for the first panel. + + Returns + ------- + fig : matplotlib.figure.Figure + axes : ndarray of matplotlib.axes.Axes + """ + wave = _as_float_array(wave) + flux = _as_float_array(flux) + model = _as_float_array(model) + + if wave.shape != flux.shape or wave.shape != model.shape: + raise ValueError("wave, flux, and model must have the same shape.") + + if err is not None: + err = _as_float_array(err) + if err.shape != wave.shape: + raise ValueError("err must match wave shape.") + + if excluded_mask is not None: + excluded_mask = _as_bool_array(excluded_mask, n_expected=wave.size) + + nwin = len(windows) + ncols = max(1, int(ncols)) + nrows = int(np.ceil(nwin / ncols)) + + fig, axes = plt.subplots( + nrows, + ncols, + figsize=(figsize_per_panel[0] * ncols, figsize_per_panel[1] * nrows), + squeeze=False, + ) + + first_visible = True + + for ax, win in zip(axes.ravel(), windows): + if len(win) == 3: + label, wmin, wmax = win + elif len(win) == 2: + wmin, wmax = win + label = None + else: + raise ValueError("Each window must be (label, wmin, wmax) or (wmin, wmax).") + + m = (wave >= wmin) & (wave <= wmax) + if not m.any(): + ax.set_visible(False) + continue + + if first_visible: + ax.plot(wave[m], flux[m], lw=0.9, label=data_label) + ax.plot(wave[m], model[m], lw=0.9, label=model_label) + first_visible = False + else: + ax.plot(wave[m], flux[m], lw=0.9) + ax.plot(wave[m], model[m], lw=0.9) + + if excluded_mask is not None and excluded_mask.any(): + for swmin, swmax in _mask_to_spans(wave, excluded_mask & m): + ax.axvspan(swmin, swmax, alpha=0.12) + + ylo, yhi = _compute_robust_ylim(flux[m], lower=2.0, upper=98.0, pad_frac=0.08) + ax.set_ylim(ylo, yhi) + ax.set_xlim(wmin, wmax) + + if label is not None: + ax.set_title(label) + + if line_groups is not None: + for group in line_groups: + lines = _resolve_line_group(group) + mark_lines(ax, lines, xlim=(wmin, wmax), ymax=0.98) + + for ax in axes.ravel()[nwin:]: + ax.set_visible(False) + + handles, labels = axes.ravel()[0].get_legend_handles_labels() + if labels: + axes.ravel()[0].legend(frameon=False, loc="best") + + if title is not None: + fig.suptitle(title) + fig.tight_layout(rect=[0, 0, 1, 0.97]) + else: + fig.tight_layout() + + return fig, axes + + +def plot_spectrum_quicklook( + spec, + use_mask=True, + show_error=False, + ax=None, + title=None, +): + """ + Very basic quick-look plot for a spectrum-like object. + + Parameters + ---------- + spec : SpectrumSegment or tuple-like + Either an object with .wave, .flux, .err, .mask attributes, + or a tuple/list of (wave, flux[, err[, mask]]). + use_mask : bool, optional + If True, plot only pixels where mask is True. + show_error : bool, optional + If True and errors are available, overplot the error array. + ax : matplotlib Axes, optional + Existing axis to draw on. If None, create a new figure/axis. + title : str, optional + Plot title. + + Returns + ------- + fig, ax + """ + if hasattr(spec, "wave") and hasattr(spec, "flux"): + wave = _as_float_array(spec.wave) + flux = _as_float_array(spec.flux) + err = None if getattr(spec, "err", None) is None else _as_float_array(spec.err) + mask = None if getattr(spec, "mask", None) is None else _as_bool_array(spec.mask) + name = getattr(spec, "name", None) + else: + wave = _as_float_array(spec[0]) + flux = _as_float_array(spec[1]) + err = None + mask = None + name = None + if len(spec) > 2 and spec[2] is not None: + err = _as_float_array(spec[2]) + if len(spec) > 3 and spec[3] is not None: + mask = _as_bool_array(spec[3]) + + good = np.isfinite(wave) & np.isfinite(flux) + if use_mask and mask is not None: + good &= mask + if err is not None and show_error: + good &= np.isfinite(err) + + if ax is None: + fig, ax = plt.subplots(figsize=(10, 4)) + else: + fig = ax.figure + + ax.plot(wave[good], flux[good], lw=0.8) + + if show_error and err is not None: + ax.plot(wave[good], err[good], lw=0.6, alpha=0.8) + + ax.set_xlabel("Wavelength [Å]") + ax.set_ylabel("Flux") + + if title is not None: + ax.set_title(title) + elif name is not None: + ax.set_title(str(name)) + + return fig, ax diff --git a/Spyctres/recipes.py b/Spyctres/recipes.py new file mode 100644 index 0000000..0050652 --- /dev/null +++ b/Spyctres/recipes.py @@ -0,0 +1,1373 @@ +""" +Workflow-level fitting recipes built on top of Spyctres core primitives. + +This module is intentionally higher-level than Spyctres.fitting. It contains +maintained recipe helpers that are useful for real workflows and examples, +but are more specialized than the generic full-spectrum fitter. + +Current scope +------------- +- X-SHOOTER/Balmer-window helper presets +- sideband normalization for line-window workflows +- sideband-aware PHOENIX fitting on top of the native-grid forward model +- reconstruction of fitted models for plotting + +These helpers operate on generic SpectrumSegment objects, so the logic is not +strictly tied to one instrument even when some presets are X-SHOOTER-oriented. +""" + +import numpy as np +from scipy.optimize import least_squares + +from .io import SpectrumSegment, make_padded_window_segments +from .waveutils import convert_wavelength_medium, C_KMS +from .fitting import ( + build_effective_fit_mask, + build_excluded_mask, + reconstruct_phoenix_legendre_models_for_segments, + _resolve_broadening_fwhm_kms, + _resolve_segment_fwhm_kms, + _gaussian_broaden_velocity, + _apply_observed_grid_rv_shift, +) +from .phoenix_forward import ( + build_phoenix_native_models_for_segments, + infer_segments_wave_medium, + build_native_interp_wave_grid_for_segments, +) + +BALMER_CENTERS_VAC = { + "Hα": 6562.80, + "Hβ": 4861.33, + "Hγ": 4340.47, + "Hδ": 4101.74, +} + +XSHOOTER_BALMER_WINDOWS = { + "current": [ + ("Hδ", 4076.0, 4128.0), + ("Hγ", 4314.0, 4366.0), + ("Hβ", 4836.0, 4888.0), + ], + "notebook": [ + ("Hδ", 3980.0, 4220.0), + ("Hγ", 4220.0, 4480.0), + ("Hβ", 4700.0, 5020.0), + ], +} + +XSHOOTER_NOTEBOOK_CONT_WINDOWS = { + "Hδ": ((-80.0, -30.0), (30.0, 80.0)), + "Hγ": ((-80.0, -30.0), (30.0, 80.0)), + "Hβ": ((-120.0, -40.0), (40.0, 120.0)), +} + +XSHOOTER_BALMER_CORE_MASK_DEFAULT_A = 10.0 +XSHOOTER_BALMER_CORE_MASK_CONSERVATIVE_A = 12.0 + +BALMER_LABEL_ALIASES = { + "hα": "Hα", + "halpha": "Hα", + "ha": "Hα", + "hβ": "Hβ", + "hbeta": "Hβ", + "hb": "Hβ", + "hγ": "Hγ", + "hgamma": "Hγ", + "hg": "Hγ", + "hδ": "Hδ", + "hdelta": "Hδ", + "hd": "Hδ", +} + + +def xshooter_balmer_windows(window_mode="notebook"): + """ + Return X-SHOOTER UVB Balmer-window presets. + + Parameters + ---------- + window_mode : {"current", "notebook"} + Current narrow windows or broader notebook-style windows. + + Returns + ------- + list of (label, wmin, wmax) + A fresh list of preset UVB Balmer windows. + """ + mode = str(window_mode).strip().lower() + if mode not in XSHOOTER_BALMER_WINDOWS: + raise ValueError("window_mode must be 'current' or 'notebook'.") + return list(XSHOOTER_BALMER_WINDOWS[mode]) + + +def _canonical_balmer_label(label): + """ + Normalize common ASCII and Unicode Balmer-line aliases to a canonical label. + + Examples + -------- + Halpha -> Hα + Hbeta -> Hβ + Hgamma -> Hγ + Hdelta -> Hδ + """ + raw = str(label).strip() + key = raw.lower().replace("_", "").replace("-", "").replace(" ", "") + return BALMER_LABEL_ALIASES.get(key, raw) + + +def _canonicalize_balmer_dict_keys(d): + """ + Return a copy of a dict whose keys are Balmer-line labels normalized to + the canonical internal form. + """ + if d is None: + return {} + return {_canonical_balmer_label(k): v for k, v in d.items()} + + +def attach_balmer_metadata(segments, cont_windows=None, centers_vac=None): + """ + Attach Balmer-line metadata to segments in-place. + + This helper accepts both canonical Unicode labels such as 'Hβ', 'Hγ', and + common ASCII aliases such as 'Hbeta', 'Hgamma', 'Hdelta', 'Halpha'. + + For each segment it stores: + - line_label : canonical internal label + - line_label_input : original input label + - line_center_vac : vacuum line center + - line_center_data : line center converted into the segment wavelength medium + - cont_windows : continuum sideband windows, if available + + Parameters + ---------- + segments : list[SpectrumSegment] + Input line-window segments. + cont_windows : dict, optional + Mapping from line label to continuum sideband windows. + Defaults to XSHOOTER_NOTEBOOK_CONT_WINDOWS. + centers_vac : dict, optional + Mapping from line label to vacuum line center in Angstrom. + Defaults to BALMER_CENTERS_VAC. + + Returns + ------- + list[SpectrumSegment] + The same list, for convenience. + """ + if cont_windows is None: + cont_windows = XSHOOTER_NOTEBOOK_CONT_WINDOWS + if centers_vac is None: + centers_vac = BALMER_CENTERS_VAC + + cont_windows = _canonicalize_balmer_dict_keys(cont_windows) + centers_vac = _canonicalize_balmer_dict_keys(centers_vac) + + for seg in segments: + raw_label = str(seg.name) + label = _canonical_balmer_label(raw_label) + + if label not in centers_vac: + raise ValueError( + "Segment name {0!r} not recognized as a supported Balmer label.".format(raw_label) + ) + + center_vac = float(centers_vac[label]) + + seg.meta["line_label_input"] = raw_label + seg.meta["line_label"] = label + seg.meta["line_center_vac"] = center_vac + + seg_medium = str(seg.wave_medium).lower() + if seg_medium in ("air", "vacuum"): + center_data = float( + convert_wavelength_medium( + np.array([center_vac], dtype=float), + from_medium="vacuum", + to_medium=seg_medium, + )[0] + ) + else: + center_data = center_vac + + seg.meta["line_center_data"] = center_data + + if label in cont_windows: + seg.meta["cont_windows"] = cont_windows[label] + else: + seg.meta.pop("cont_windows", None) + + return segments + + +def ensure_phoenix_interpolator_for_segments( + segments, + phoenix_lib, + teff_grid, + feh_grid, + logg_grid, + cache_path=None, +): + """ + Ensure the PHOENIX interpolator is built on the concatenated support grid + of the current segments. + """ + support_wave_all = np.concatenate([np.asarray(seg.wave, dtype=float) for seg in segments]) + + segment_media = sorted(set(str(seg.wave_medium).lower() for seg in segments)) + observed_wave_medium = segment_media[0] if len(segment_media) == 1 else None + + need_rebuild = False + if phoenix_lib.wave is None: + need_rebuild = True + elif (len(phoenix_lib.wave) != len(support_wave_all)) or ( + not np.allclose(phoenix_lib.wave, support_wave_all, rtol=0.0, atol=0.0) + ): + need_rebuild = True + elif phoenix_lib._grid is None: + need_rebuild = True + else: + tg, zg, gg = phoenix_lib._grid + if ( + (len(tg) != len(teff_grid)) or + (len(zg) != len(feh_grid)) or + (len(gg) != len(logg_grid)) or + (not np.allclose(tg, np.asarray(teff_grid, dtype=float), rtol=0.0, atol=0.0)) or + (not np.allclose(zg, np.asarray(feh_grid, dtype=float), rtol=0.0, atol=0.0)) or + (not np.allclose(gg, np.asarray(logg_grid, dtype=float), rtol=0.0, atol=0.0)) + ): + need_rebuild = True + + if need_rebuild: + phoenix_lib.build_interpolator( + observed_wave=support_wave_all, + teff_grid=np.asarray(teff_grid, dtype=float), + feh_grid=np.asarray(feh_grid, dtype=float), + logg_grid=np.asarray(logg_grid, dtype=float), + cache_path=cache_path, + observed_wave_medium=observed_wave_medium, + ) + + return support_wave_all + + +def _build_sideband_mask(seg, wave, fit_mask, sideband_width=10.0): + """ + Build a sideband mask for one segment. + + If seg.meta['cont_windows'] is present, use those explicit sidebands + relative to seg.meta['line_center_data']. Otherwise fall back to + edge sidebands of the fit window. + """ + wave = np.asarray(wave, dtype=float) + fit_mask = np.asarray(fit_mask, dtype=bool) + + cont_windows = seg.meta.get("cont_windows", None) + center = seg.meta.get("line_center_data", None) + + if cont_windows is not None and center is not None: + sb_mask = np.zeros_like(wave, dtype=bool) + center = float(center) + for a, b in cont_windows: + sb_mask |= fit_mask & (wave > center + float(a)) & (wave < center + float(b)) + sb_mode = "explicit" + lo = float(np.min(wave[fit_mask])) if np.any(fit_mask) else np.nan + hi = float(np.max(wave[fit_mask])) if np.any(fit_mask) else np.nan + return sb_mask, sb_mode, lo, hi + + fit_wave = wave[fit_mask] + lo = float(np.min(fit_wave)) + hi = float(np.max(fit_wave)) + sb_mask = fit_mask & ( + ((wave >= lo) & (wave <= lo + float(sideband_width))) | + ((wave >= hi - float(sideband_width)) & (wave <= hi)) + ) + return sb_mask, "edge", lo, hi + + +def normalize_segment_sidebands(seg, sideband_width=10.0, sideband_order=1): + """ + Normalize one segment using a weighted polynomial continuum fit to either: + - explicit per-line sidebands stored in seg.meta['cont_windows'], or + - fallback edge sidebands of the fit window. + + Returns + ------- + seg_n : SpectrumSegment + Sideband-normalized segment. + info : dict + Small diagnostic dictionary about the normalization. + """ + wave = np.asarray(seg.wave, dtype=float) + flux = np.asarray(seg.flux, dtype=float) + err = None if seg.err is None else np.asarray(seg.err, dtype=float) + fit_mask = np.asarray(seg.mask, dtype=bool) + + if np.sum(fit_mask) < 6: + return seg, {"mode": "none", "n_sideband": 0} + + sb_mask, sb_mode, lo, hi = _build_sideband_mask( + seg, wave, fit_mask, sideband_width=sideband_width + ) + + good = sb_mask & np.isfinite(wave) & np.isfinite(flux) + if err is not None: + good &= np.isfinite(err) & (err > 0) + + order = int(sideband_order) + + if np.sum(good) >= (order + 2): + if err is None: + coeffs = np.polyfit(wave[good], flux[good], deg=order) + else: + coeffs = np.polyfit(wave[good], flux[good], deg=order, w=1.0 / err[good]) + cont = np.polyval(coeffs, wave) + mode = "poly" + else: + level = float(np.nanmedian(flux[fit_mask])) + coeffs = np.array([level], dtype=float) + cont = np.full_like(wave, level, dtype=float) + mode = "constant" + + pos = np.isfinite(cont) & (cont > 0) + if not np.any(pos): + raise ValueError("Sideband normalization produced a non-positive continuum.") + + fallback = float(np.nanmedian(cont[pos])) + cont = np.where(np.isfinite(cont) & (cont > 0), cont, fallback) + + flux_n = flux / cont + err_n = None if err is None else err / cont + + seg_n = SpectrumSegment( + wave=wave, + flux=flux_n, + err=err_n, + mask=fit_mask, + meta=dict(seg.meta), + wave_medium=seg.wave_medium, + wave_frame=seg.wave_frame, + name=seg.name, + ) + + seg_n.meta["norm_mode"] = "sideband" + seg_n.meta["sideband_width"] = float(sideband_width) + seg_n.meta["sideband_order"] = int(sideband_order) + seg_n.meta["sideband_cont_coeffs"] = np.asarray(coeffs, dtype=float).tolist() + + info = { + "mode": mode, + "sideband_mode": sb_mode, + "n_sideband": int(np.sum(good)), + "fit_lo": lo, + "fit_hi": hi, + "coeffs": coeffs, + } + return seg_n, info + + +def normalize_segments_sidebands(segments, sideband_width=10.0, sideband_order=1): + """ + Apply sideband normalization to a list of segments. + + Returns + ------- + segments_n : list[SpectrumSegment] + info : list[dict] + """ + out = [] + info = [] + for seg in segments: + seg_n, seg_info = normalize_segment_sidebands( + seg, + sideband_width=sideband_width, + sideband_order=sideband_order, + ) + out.append(seg_n) + info.append(seg_info) + return out, info + + +def normalize_model_sidebands(seg, model_flux, sideband_width=10.0, sideband_order=1): + """ + Normalize a model array on a segment grid using the same sideband logic + as the data-side normalization. + + Returns + ------- + model_n : ndarray + info : dict + """ + wave = np.asarray(seg.wave, dtype=float) + model_flux = np.asarray(model_flux, dtype=float) + fit_mask = np.asarray(seg.mask, dtype=bool) + + if np.sum(fit_mask) < 6: + return model_flux.copy(), {"mode": "none", "n_sideband": 0} + + sb_mask, sb_mode, lo, hi = _build_sideband_mask( + seg, wave, fit_mask, sideband_width=sideband_width + ) + + good = sb_mask & np.isfinite(wave) & np.isfinite(model_flux) + order = int(sideband_order) + + if np.sum(good) >= (order + 2): + coeffs = np.polyfit(wave[good], model_flux[good], deg=order) + cont = np.polyval(coeffs, wave) + mode = "poly" + else: + level = float(np.nanmedian(model_flux[fit_mask])) + coeffs = np.array([level], dtype=float) + cont = np.full_like(wave, level, dtype=float) + mode = "constant" + + pos = np.isfinite(cont) & (cont > 0) + if not np.any(pos): + raise ValueError("Model sideband normalization produced a non-positive continuum.") + + fallback = float(np.nanmedian(cont[pos])) + cont = np.where(np.isfinite(cont) & (cont > 0), cont, fallback) + + model_n = model_flux / cont + info = { + "mode": mode, + "sideband_mode": sb_mode, + "n_sideband": int(np.sum(good)), + "fit_lo": lo, + "fit_hi": hi, + "coeffs": coeffs, + } + return model_n, info + + +def _solve_sideband_multiplicative_poly(wave, flux, err, model, used_mask, order=1): + """ + Solve a weighted multiplicative polynomial after sideband normalization. + + This mirrors the notebook logic: + flux ~ model * poly(w) + on the used pixels. + """ + wave = np.asarray(wave, dtype=float) + flux = np.asarray(flux, dtype=float) + err = np.asarray(err, dtype=float) + model = np.asarray(model, dtype=float) + used_mask = np.asarray(used_mask, dtype=bool) + order = int(order) + + if order <= 0: + return model.copy(), np.array([1.0], dtype=float) + + good = ( + used_mask & + np.isfinite(wave) & + np.isfinite(flux) & + np.isfinite(err) & (err > 0) & + np.isfinite(model) + ) + + if np.sum(good) < (order + 2): + return model.copy(), np.array([1.0], dtype=float) + + x0 = float(np.mean(wave[good])) + xscale = float(np.ptp(wave[good])) + if (not np.isfinite(xscale)) or (xscale <= 0): + xscale = 1.0 + + x = (wave[good] - x0) / xscale + A = np.vander(x, N=order + 1, increasing=True) + + rhs = flux[good] / (model[good] + 1e-30) + W = 1.0 / err[good] + + Aw = A * W[:, None] + bw = rhs * W + coeffs, *_ = np.linalg.lstsq(Aw, bw, rcond=None) + + x_all = (wave - x0) / xscale + poly_all = np.vander(x_all, N=order + 1, increasing=True) @ coeffs + + return model * poly_all, coeffs + + +def make_balmer_core_exclude_mask( + core_halfwidth=XSHOOTER_BALMER_CORE_MASK_DEFAULT_A, + wave_medium="vacuum", +): + """ + Build a boolean exclusion-mask callable for the UVB Balmer line cores. + + This helper currently targets the three Balmer lines used by the X-SHOOTER + UVB recipes: Hδ, Hγ, and Hβ. + + Parameters + ---------- + core_halfwidth : float + Half-width in Angstrom around each line center to exclude. + For X-SHOOTER UVB Balmer-wing classification, the + default value is 10 Angstrom. Use 6–12 Angstroms as a robustness range. + wave_medium : {"air", "vacuum", "unknown"} + Wavelength medium of the observed data. + """ + centers_vac = np.array( + [ + BALMER_CENTERS_VAC["Hδ"], + BALMER_CENTERS_VAC["Hγ"], + BALMER_CENTERS_VAC["Hβ"], + ], + dtype=float, + ) + + wave_medium = str(wave_medium).lower() + if wave_medium in ("air", "vacuum"): + centers = convert_wavelength_medium( + centers_vac, + from_medium="vacuum", + to_medium=wave_medium, + ) + else: + centers = centers_vac.copy() + + def _mask(wave): + wave = np.asarray(wave, dtype=float) + m = np.zeros_like(wave, dtype=bool) + for c in centers: + m |= np.abs(wave - c) <= float(core_halfwidth) + return m + + return _mask + + +def fit_phoenix_sideband_symmetric( + segments, + phoenix_lib, + p0, + exclude_mask=None, + rv_bary_kms=0.0, + R=None, + forward_model="interp_observed", + model_margin_A=200.0, + teff_grid=None, + feh_grid=None, + logg_grid=None, + cache_path=None, + rv_init="grid", + rv_grid_n=81, + verbose=1, + max_nfev=200, + sideband_width=10.0, + sideband_order=1, + sideband_poly_order=1, + bounds=None, +): + """ + Sideband-normalized fitter for line-window workflows. + + Data are sideband-normalized segment-by-segment, the model is + sideband-normalized the same way, and a low-order multiplicative polynomial + is then solved on the used pixels before residuals are computed. + + The wavelength-space forward model can follow either: + - forward_model="interp_observed": interpolate directly on the segment + support grid, then apply the PHOENIX RV convention and broaden there. + This is retained as a fast/legacy compatibility path. + - forward_model="native_interp": interpolate on a dense model-space + wavelength grid, then shift, convolve, and resample last. This is the + recommended path for line-profile work. + + RV convention + ------------- + The returned `rv_kms` follows the PHOENIX fitting convention used by + Spyctres.fitting: positive RV redshifts the template/model. The observed-grid + branch uses `_apply_observed_grid_rv_shift()` internally to preserve this + convention while leaving the legacy Spyctres.velocity_correction API unchanged. + """ + if isinstance(segments, SpectrumSegment): + segments = [segments] + else: + segments = list(segments) + + for seg in segments: + if seg.err is None: + raise ValueError( + "fit_phoenix_sideband_symmetric requires seg.err for all segments. " + "Provide uncertainties or use fit_phoenix_full_spectrum(), which " + "can estimate fallback errors." + ) + + teff0, feh0, logg0, rv0 = map(float, p0) + + if teff_grid is None: + teff_grid_req = phoenix_lib.DEFAULT_TEFF_GRID + else: + teff_grid_req = np.asarray(teff_grid, dtype=float) + + if feh_grid is None: + feh_grid_req = phoenix_lib.DEFAULT_FEH_GRID + else: + feh_grid_req = np.asarray(feh_grid, dtype=float) + + if logg_grid is None: + logg_grid_req = phoenix_lib.DEFAULT_LOGG_GRID + else: + logg_grid_req = np.asarray(logg_grid, dtype=float) + + if forward_model not in ("interp_observed", "native_interp"): + raise ValueError("forward_model must be 'interp_observed' or 'native_interp'.") + + used_masks = [ + build_effective_fit_mask(seg, exclude_mask=exclude_mask) + for seg in segments + ] + if not any(np.any(m) for m in used_masks): + raise ValueError("No usable points remain after masking.") + + support_lengths = [len(seg.wave) for seg in segments] + segment_fwhm_kms = [ + _resolve_segment_fwhm_kms(seg, R=R, fwhm_kms=None) + for seg in segments + ] + if forward_model == "interp_observed": + support_wave_all = ensure_phoenix_interpolator_for_segments( + segments=segments, + phoenix_lib=phoenix_lib, + teff_grid=teff_grid_req, + feh_grid=feh_grid_req, + logg_grid=logg_grid_req, + cache_path=cache_path, + ) + else: + model_wave_grid, model_wave_medium = build_native_interp_wave_grid_for_segments( + segments=segments, + phoenix_lib=phoenix_lib, + model_margin_A=model_margin_A, + ) + + need_rebuild = False + if phoenix_lib.wave is None: + need_rebuild = True + elif (len(phoenix_lib.wave) != len(model_wave_grid)) or ( + not np.allclose(phoenix_lib.wave, model_wave_grid, rtol=0.0, atol=0.0) + ): + need_rebuild = True + elif phoenix_lib._grid is None: + need_rebuild = True + else: + tg, zg, gg = phoenix_lib._grid + if ( + (len(tg) != len(teff_grid_req)) or + (len(zg) != len(feh_grid_req)) or + (len(gg) != len(logg_grid_req)) or + (not np.allclose(tg, teff_grid_req, rtol=0.0, atol=0.0)) or + (not np.allclose(zg, feh_grid_req, rtol=0.0, atol=0.0)) or + (not np.allclose(gg, logg_grid_req, rtol=0.0, atol=0.0)) + ): + need_rebuild = True + + if need_rebuild: + phoenix_lib.build_interpolator( + observed_wave=model_wave_grid, + teff_grid=teff_grid_req, + feh_grid=feh_grid_req, + logg_grid=logg_grid_req, + cache_path=cache_path, + observed_wave_medium=model_wave_medium, + ) + + if bounds is None: + tg, zg, gg = phoenix_lib._grid + bounds = ( + (float(np.min(tg)), float(np.min(zg)), float(np.min(gg)), -300.0), + (float(np.max(tg)), float(np.max(zg)), float(np.max(gg)), +300.0), + ) + + broadening_fwhm_kms = _resolve_broadening_fwhm_kms(R=R, fwhm_kms=None) + n_points = int(sum(np.sum(m) for m in used_masks)) + + def residuals(p): + teff, feh, logg, rv_kms = map(float, p) + + try: + model0 = np.asarray(phoenix_lib.evaluate(teff, feh, logg), dtype=float) + except Exception: + return np.ones(n_points, dtype=float) * 1e6 + + out = [] + + if forward_model == "interp_observed": + rv_tot = float(rv_bary_kms) + float(rv_kms) + if len(model0) != len(support_wave_all): + return np.ones(n_points, dtype=float) * 1e6 + + shifted_all = _apply_observed_grid_rv_shift( + support_wave_all, + model0, + rv_tot, + ) + + i0 = 0 + for seg, used_mask, n_support, seg_fwhm in zip( + segments, used_masks, support_lengths, segment_fwhm_kms + ): + i1 = i0 + n_support + + wave = np.asarray(seg.wave, dtype=float) + flux = np.asarray(seg.flux, dtype=float) + err = np.asarray(seg.err, dtype=float) + + model_full = shifted_all[i0:i1] + model_full = _gaussian_broaden_velocity( + wave, + model_full, + fwhm_kms=seg_fwhm, + ) + model_norm, _ = normalize_model_sidebands( + seg, + model_full, + sideband_width=sideband_width, + sideband_order=sideband_order, + ) + + model_corr, _ = _solve_sideband_multiplicative_poly( + wave=wave, + flux=flux, + err=err, + model=model_norm, + used_mask=used_mask, + order=sideband_poly_order, + ) + + out.append((flux[used_mask] - model_corr[used_mask]) / err[used_mask]) + i0 = i1 + + else: + model_list = build_phoenix_native_models_for_segments( + segments=segments, + phoenix_wave_native=np.asarray(phoenix_lib.wave, dtype=float), + template_flux_native=model0, + rv_kms=rv_kms, + rv_bary_kms=rv_bary_kms, + segment_fwhm_kms=segment_fwhm_kms, + phoenix_wave_medium=model_wave_medium, + model_margin_A=model_margin_A, + bounds_use_fit_mask=True, + extrapolate=True, + ) + for seg, used_mask, model_full in zip(segments, used_masks, model_list): + wave = np.asarray(seg.wave, dtype=float) + flux = np.asarray(seg.flux, dtype=float) + err = np.asarray(seg.err, dtype=float) + + model_norm, _ = normalize_model_sidebands( + seg, + model_full, + sideband_width=sideband_width, + sideband_order=sideband_order, + ) + + model_corr, _ = _solve_sideband_multiplicative_poly( + wave=wave, + flux=flux, + err=err, + model=model_norm, + used_mask=used_mask, + order=sideband_poly_order, + ) + + out.append((flux[used_mask] - model_corr[used_mask]) / err[used_mask]) + + return np.concatenate(out) + + if rv_init == "grid": + rv_lo, rv_hi = float(bounds[0][3]), float(bounds[1][3]) + rv_grid = np.linspace(rv_lo, rv_hi, int(rv_grid_n)) + chi2s = np.array( + [np.sum(residuals((teff0, feh0, logg0, float(rv))) ** 2) for rv in rv_grid], + dtype=float, + ) + rv0_use = float(rv_grid[np.argmin(chi2s)]) + if verbose: + print("RV init grid best:", rv0_use) + p0_use = (teff0, feh0, logg0, rv0_use) + elif rv_init is None: + p0_use = (teff0, feh0, logg0, rv0) + else: + raise ValueError("rv_init must be 'grid' or None.") + + res = least_squares( + residuals, + x0=np.array(p0_use, dtype=float), + bounds=bounds, + method="trf", + x_scale=np.array([100.0, 0.1, 0.1, 10.0], dtype=float), + max_nfev=int(max_nfev), + verbose=2 if verbose else 0, + ) + + r = res.fun + chi2 = float(np.sum(r * r)) + n = int(r.size) + k = 4 + dof = max(1, n - k) + chi2_red = chi2 / dof + + return { + "success": bool(res.success), + "message": res.message, + "p_best": res.x, + "teff": float(res.x[0]), + "feh": float(res.x[1]), + "logg": float(res.x[2]), + "rv_kms": float(res.x[3]), + "rv_bary_kms": float(rv_bary_kms), + "chi2": chi2, + "dof": dof, + "chi2_red": chi2_red, + "n_points": n, + "status": int(res.status), + "nfev": int(res.nfev), + "forward_model": str(forward_model), + "model_margin_A": float(model_margin_A), + "segment_lsf_fwhm_kms": [ + None if x is None else float(x) for x in segment_fwhm_kms + ], + "segment_resolution_R_effective": [ + None if x is None else float(C_KMS / x) for x in segment_fwhm_kms + ], + "resolution_R": None if R is None else float(R), + "lsf_fwhm_kms": None if broadening_fwhm_kms is None else float(broadening_fwhm_kms), + } + + +def build_plot_models_for_segments( + segments, + phoenix_lib, + fit_result, + exclude_mask=None, + mdeg=2, + rv_bary_kms=0.0, + R=None, + fwhm_kms=None, + norm_mode="poly", + sideband_width=10.0, + sideband_order=1, + sideband_poly_order=1, + forward_model=None, + model_margin_A=None, +): + """ + Reconstruct per-segment fitted model arrays on the full pixel grid of each segment. + + Parameters + ---------- + norm_mode : {"poly", "sideband"} + Reconstruction path. "poly" delegates to the generic fitter-side + polynomial reconstruction. "sideband" rebuilds the sideband-normalized + model and the local multiplicative polynomial used by the recipe fitter. + """ + teff = float(fit_result["teff"]) + feh = float(fit_result["feh"]) + logg = float(fit_result["logg"]) + rv_kms = float(fit_result["rv_kms"]) + + if forward_model is None: + forward_model = str(fit_result.get("forward_model", "interp_observed")) + if model_margin_A is None: + model_margin_A = float(fit_result.get("model_margin_A", 200.0)) + + if norm_mode == "poly": + return reconstruct_phoenix_legendre_models_for_segments( + segments=segments, + phoenix_lib=phoenix_lib, + fit_result=fit_result, + exclude_mask=exclude_mask, + mdeg=mdeg, + rv_bary_kms=rv_bary_kms, + R=R, + fwhm_kms=fwhm_kms, + forward_model=forward_model, + model_margin_A=model_margin_A, + ) + + if norm_mode != "sideband": + raise ValueError("norm_mode must be 'poly' or 'sideband'.") + + used_masks = [ + build_effective_fit_mask(seg, exclude_mask=exclude_mask) + for seg in segments + ] + excluded_masks = [ + build_excluded_mask(seg, exclude_mask=exclude_mask) + for seg in segments + ] + segment_fwhm_kms = [ + _resolve_segment_fwhm_kms(seg, R=R, fwhm_kms=fwhm_kms) + for seg in segments + ] + model_full_list = [] + coeffs_list = [] + + if forward_model == "interp_observed": + support_lengths = [len(seg.wave) for seg in segments] + n_support_total = int(sum(support_lengths)) + + model_support_all = np.asarray(phoenix_lib.evaluate(teff, feh, logg), dtype=float) + if len(model_support_all) != n_support_total: + raise ValueError( + "Model grid length does not match total support wavelength grid: " + "{0} vs {1}".format(len(model_support_all), n_support_total) + ) + + i0 = 0 + for seg, used_mask, seg_fwhm in zip(segments, used_masks, segment_fwhm_kms): + wave_full = np.asarray(seg.wave, dtype=float) + flux_full = np.asarray(seg.flux, dtype=float) + err_full = np.asarray(seg.err, dtype=float) + + n_support = len(wave_full) + i1 = i0 + n_support + + model0_full = model_support_all[i0:i1] + + shifted_full = _apply_observed_grid_rv_shift( + wave_full, + model0_full, + rv_bary_kms + rv_kms, + ) + model_broad_full = _gaussian_broaden_velocity( + wave_full, + shifted_full, + fwhm_kms=seg_fwhm, + ) + model_norm_full, _ = normalize_model_sidebands( + seg, + model_broad_full, + sideband_width=sideband_width, + sideband_order=sideband_order, + ) + + model_corr_full, coeffs = _solve_sideband_multiplicative_poly( + wave=wave_full, + flux=flux_full, + err=err_full, + model=model_norm_full, + used_mask=used_mask, + order=sideband_poly_order, + ) + + model_full_list.append(model_corr_full.copy()) + coeffs_list.append(coeffs) + i0 = i1 + + elif forward_model == "native_interp": + model_dense = np.asarray(phoenix_lib.evaluate(teff, feh, logg), dtype=float) + + model_wave_medium = infer_segments_wave_medium( + segments, + default=getattr(phoenix_lib, "phoenix_wave_medium", "vacuum"), + ) + + model_raw_list = build_phoenix_native_models_for_segments( + segments=segments, + phoenix_wave_native=np.asarray(phoenix_lib.wave, dtype=float), + template_flux_native=model_dense, + rv_kms=rv_kms, + rv_bary_kms=rv_bary_kms, + segment_fwhm_kms=segment_fwhm_kms, + phoenix_wave_medium=model_wave_medium, + model_margin_A=model_margin_A, + bounds_use_fit_mask=True, + extrapolate=True, + ) + for seg, used_mask, model_broad_full in zip(segments, used_masks, model_raw_list): + wave_full = np.asarray(seg.wave, dtype=float) + flux_full = np.asarray(seg.flux, dtype=float) + err_full = np.asarray(seg.err, dtype=float) + + model_norm_full, _ = normalize_model_sidebands( + seg, + model_broad_full, + sideband_width=sideband_width, + sideband_order=sideband_order, + ) + + model_corr_full, coeffs = _solve_sideband_multiplicative_poly( + wave=wave_full, + flux=flux_full, + err=err_full, + model=model_norm_full, + used_mask=used_mask, + order=sideband_poly_order, + ) + + model_full_list.append(model_corr_full.copy()) + coeffs_list.append(coeffs) + else: + raise ValueError("Unknown forward_model: {0}".format(forward_model)) + + return model_full_list, coeffs_list, used_masks, excluded_masks + + +PEPSI_LEGACY_CENTERS_AIR = [6495.0, 6545.0, 6561.0, 8498.0, 8542.0, 8662.0] + + +def build_pepsi_legacy_windows(centers_air=None, halfwidth_A=10.0): + centers = PEPSI_LEGACY_CENTERS_AIR if centers_air is None else centers_air + out = [] + for c in centers: + c = float(c) + out.append(("legacy_{0:.1f}".format(c), c - float(halfwidth_A), c + float(halfwidth_A))) + return out + + +def convert_air_windows_to_medium(window_defs_air, to_medium): + to_medium = str(to_medium).strip().lower() + + if to_medium in ("air", "unknown", ""): + return list(window_defs_air) + + if to_medium != "vacuum": + raise ValueError("Unsupported wavelength medium: {0}".format(to_medium)) + + out = [] + for label, wmin_air, wmax_air in window_defs_air: + w_air = np.array([float(wmin_air), float(wmax_air)], dtype=float) + w_new = convert_wavelength_medium(w_air, from_medium="air", to_medium="vacuum") + out.append((label, float(w_new[0]), float(w_new[1]))) + + return out + + +def apply_pepsi_wave_hypothesis(seg, hypothesis): + hypothesis = str(hypothesis).strip().lower() + + if hypothesis == "unknown": + meta = dict(seg.meta) + meta["wave_medium"] = "unknown" + return seg.copy(meta=meta, wave_medium="unknown", name=(seg.name or "seg") + "_unknown") + + if hypothesis == "air": + meta = dict(seg.meta) + meta["wave_medium"] = "air" + return seg.copy(meta=meta, wave_medium="air", name=(seg.name or "seg") + "_air") + + if hypothesis == "vacuum": + meta = dict(seg.meta) + meta["wave_medium"] = "vacuum" + return seg.copy(meta=meta, wave_medium="vacuum", name=(seg.name or "seg") + "_vacuum") + + if hypothesis == "air_to_vac": + wave_new = convert_wavelength_medium( + np.asarray(seg.wave, dtype=float), + from_medium="air", + to_medium="vacuum", + ) + meta = dict(seg.meta) + meta["wave_medium"] = "vacuum" + return seg.copy( + wave=wave_new, + meta=meta, + wave_medium="vacuum", + name=(seg.name or "seg") + "_air2vac", + ).sorted() + + raise ValueError("Unknown wavelength hypothesis: {0}".format(hypothesis)) + + +def build_pepsi_normalized_mask(seg, flux_min=0.2, flux_max=1.1): + wave = np.asarray(seg.wave, dtype=float) + flux = np.asarray(seg.flux, dtype=float) + + good = np.asarray(seg.mask, dtype=bool) + good &= np.isfinite(wave) & np.isfinite(flux) + good &= (flux > float(flux_min)) & (flux < float(flux_max)) + + if seg.err is not None: + err = np.asarray(seg.err, dtype=float) + good &= np.isfinite(err) & (err > 0) + + return good + + +def build_pepsi_legacy_segments( + input_segments, + wave_hypothesis="air", + centers_air=None, + halfwidth_A=10.0, + flux_min=0.2, + flux_max=1.1, + window_pad_A=2.0, +): + window_defs_air = build_pepsi_legacy_windows( + centers_air=centers_air, + halfwidth_A=halfwidth_A, + ) + + working_input_segments = [] + fit_segments = [] + + for seg0 in input_segments: + legacy_mask = build_pepsi_normalized_mask( + seg0, + flux_min=flux_min, + flux_max=flux_max, + ) + seg0 = seg0.copy(mask=legacy_mask) + seg = apply_pepsi_wave_hypothesis(seg0, wave_hypothesis) + working_input_segments.append(seg) + + working_window_defs = convert_air_windows_to_medium( + window_defs_air, + to_medium=seg.wave_medium, + ) + + wlo = float(np.nanmin(seg.wave)) + whi = float(np.nanmax(seg.wave)) + present_defs = [ + (label, wmin, wmax) + for label, wmin, wmax in working_window_defs + if (wmax >= wlo) and (wmin <= whi) + ] + + if len(present_defs) == 0: + continue + + seg_windows = make_padded_window_segments( + seg, + [(wmin, wmax) for _label, wmin, wmax in present_defs], + pad=window_pad_A, + name_prefix="line", + ) + + for sw, win_def in zip(seg_windows, present_defs): + sw.name = win_def[0] + sw.meta["source_file"] = seg.meta.get("source_file") + sw.meta["legacy_window_air"] = tuple( + x for x in next(w for w in window_defs_air if w[0] == win_def[0]) + ) + sw.meta["legacy_window_working"] = tuple(win_def) + sw.meta["legacy_window_medium"] = seg.wave_medium + + fit_segments.extend(seg_windows) + + if len(fit_segments) == 0: + raise ValueError("No PEPSI legacy line windows overlap the supplied segment(s).") + + return working_input_segments, fit_segments, window_defs_air + + +def make_pepsi_legacy_cache_support_segments( + input_segments, + window_defs_air, + window_pad_A=2.0, +): + support_segments = [] + + for seg in input_segments: + working_window_defs = convert_air_windows_to_medium( + window_defs_air, + to_medium=seg.wave_medium, + ) + + wlo = float(np.nanmin(seg.wave)) + whi = float(np.nanmax(seg.wave)) + + present_defs = [ + (label, wmin, wmax) + for label, wmin, wmax in working_window_defs + if (wmax >= wlo) and (wmin <= whi) + ] + + for label, wmin, wmax in present_defs: + keep = ( + np.isfinite(seg.wave) & + (seg.wave >= float(wmin) - float(window_pad_A)) & + (seg.wave <= float(wmax) + float(window_pad_A)) + ) + + if not np.any(keep): + continue + + n_keep = int(np.sum(keep)) + support_segments.append( + seg.copy( + wave=seg.wave[keep], + flux=np.ones(n_keep, dtype=float), + err=np.ones(n_keep, dtype=float), + mask=np.ones(n_keep, dtype=bool), + meta=dict(seg.meta), + name="cache_support_{0}".format(label), + ) + ) + + if len(support_segments) == 0: + raise ValueError("No PEPSI legacy cache support windows overlap the supplied segment(s).") + + return support_segments + + +def evaluate_pepsi_legacy_max_models( + phoenix_lib, + segments, + model_wave_grid, + model_wave_medium, + teff, + feh, + logg, + rv_kms, + rv_bary_kms, + R, + model_margin_A, +): + template_flux = np.asarray(phoenix_lib.evaluate(teff, feh, logg), dtype=float) + segment_fwhm_kms = [segment_fwhm_kms_from_R(seg, R=R) for seg in segments] + return build_phoenix_native_models_for_segments( + segments=segments, + phoenix_wave_native=model_wave_grid, + template_flux_native=template_flux, + rv_kms=float(rv_kms), + rv_bary_kms=float(rv_bary_kms), + segment_fwhm_kms=segment_fwhm_kms, + phoenix_wave_medium=model_wave_medium, + model_margin_A=model_margin_A, + bounds_use_fit_mask=True, + extrapolate=True, + ) + + +def pepsi_legacy_max_likelihood_terms(seg, model_full, log_err_scale=0.0): + """ + Return likelihood terms for one window. + + The model is normalized by its maximum on the used pixels in the window, + matching the old model/max(model) comparison. The errors are scaled by + 10**log_err_scale and used as variances in a Gaussian negative log-likelihood. + """ + wave = np.asarray(seg.wave, dtype=float) + flux = np.asarray(seg.flux, dtype=float) + err = np.asarray(seg.err, dtype=float) + model_full = np.asarray(model_full, dtype=float) + used = np.asarray(seg.mask, dtype=bool) + used &= np.isfinite(wave) & np.isfinite(flux) & np.isfinite(err) & (err > 0) + used &= np.isfinite(model_full) + + if np.sum(used) < 4: + return np.inf, 0, np.full_like(model_full, np.nan, dtype=float), used + + mmax = float(np.nanmax(model_full[used])) + if (not np.isfinite(mmax)) or mmax == 0.0: + return np.inf, 0, np.full_like(model_full, np.nan, dtype=float), used + + model_norm = model_full / mmax + sigma = (10.0 ** float(log_err_scale)) * err[used] + var = sigma * sigma + resid = flux[used] - model_norm[used] + nll_terms = resid * resid / var + np.log(2.0 * np.pi * var) + return float(np.sum(nll_terms)), int(np.sum(used)), model_norm, used + + +def segment_fwhm_kms_from_R(seg, R=None): + if R is None: + R = getattr(seg, "meta", {}).get("resolution_R", None) + if R is None: + return None + R = float(R) + if R <= 0: + return None + return C_KMS / R + + +def ensure_phoenix_native_interpolator_for_segments( + segments, + phoenix_lib, + teff_grid, + feh_grid, + logg_grid, + cache_path=None, + model_margin_A=20.0, +): + model_wave_grid, model_wave_medium = build_native_interp_wave_grid_for_segments( + segments=segments, + phoenix_lib=phoenix_lib, + model_margin_A=model_margin_A, + ) + + teff_grid = np.asarray(teff_grid, dtype=float) + feh_grid = np.asarray(feh_grid, dtype=float) + logg_grid = np.asarray(logg_grid, dtype=float) + + need_rebuild = False + if phoenix_lib.wave is None or phoenix_lib._grid is None: + need_rebuild = True + elif (len(phoenix_lib.wave) != len(model_wave_grid)) or ( + not np.allclose(phoenix_lib.wave, model_wave_grid, rtol=0.0, atol=0.0) + ): + need_rebuild = True + else: + tg, zg, gg = phoenix_lib._grid + if ( + len(tg) != len(teff_grid) or + len(zg) != len(feh_grid) or + len(gg) != len(logg_grid) or + not np.allclose(tg, teff_grid, rtol=0.0, atol=0.0) or + not np.allclose(zg, feh_grid, rtol=0.0, atol=0.0) or + not np.allclose(gg, logg_grid, rtol=0.0, atol=0.0) + ): + need_rebuild = True + + if need_rebuild: + phoenix_lib.build_interpolator( + observed_wave=model_wave_grid, + teff_grid=teff_grid, + feh_grid=feh_grid, + logg_grid=logg_grid, + cache_path=cache_path, + observed_wave_medium=model_wave_medium, + ) + + return model_wave_grid, model_wave_medium + + +def pick_grid_range(grid, lo=None, hi=None): + g = np.asarray(grid, dtype=float) + m = np.ones_like(g, dtype=bool) + if lo is not None: + m &= (g >= float(lo)) + if hi is not None: + m &= (g <= float(hi)) + out = g[m] + if out.size == 0: + raise ValueError("Requested PHOENIX grid range is empty.") + return out + + +__all__ = [ + "BALMER_CENTERS_VAC", + "XSHOOTER_BALMER_WINDOWS", + "XSHOOTER_NOTEBOOK_CONT_WINDOWS", + "XSHOOTER_BALMER_CORE_MASK_DEFAULT_A", + "XSHOOTER_BALMER_CORE_MASK_CONSERVATIVE_A", + "xshooter_balmer_windows", + "attach_balmer_metadata", + "normalize_segment_sidebands", + "normalize_segments_sidebands", + "normalize_model_sidebands", + "make_balmer_core_exclude_mask", + "fit_phoenix_sideband_symmetric", + "build_plot_models_for_segments", + "PEPSI_LEGACY_CENTERS_AIR", + "build_pepsi_legacy_windows", + "convert_air_windows_to_medium", + "apply_pepsi_wave_hypothesis", + "build_pepsi_normalized_mask", + "build_pepsi_legacy_segments", + "make_pepsi_legacy_cache_support_segments", + "segment_fwhm_kms_from_R", + "ensure_phoenix_native_interpolator_for_segments", + "pick_grid_range", + "evaluate_pepsi_legacy_max_models", + "pepsi_legacy_max_likelihood_terms", +] diff --git a/Spyctres/waveutils.py b/Spyctres/waveutils.py new file mode 100644 index 0000000..b2a8028 --- /dev/null +++ b/Spyctres/waveutils.py @@ -0,0 +1,227 @@ +""" +Wavelength and velocity-conversion utilities for spectral fitting. + +This module provides: +- relativistic wavelength shifts for radial velocities +- air <-> vacuum wavelength conversion using the Ciddor relation +- explicit wavelength-medium conversion helpers + +Notes +----- +PHOENIX HiRes spectra are tabulated on a vacuum wavelength grid. When +comparing PHOENIX templates to observed spectra, the wavelength medium +(air or vacuum) should therefore be treated explicitly rather than assumed. + +References +---------- +Husser, T.-O., Wende-von Berg, S., Dreizler, S., Homeier, D., Reiners, A., +Barman, T., & Hauschildt, P. H. (2013), A new extensive library of PHOENIX +stellar atmospheres and synthetic spectra, Astronomy & Astrophysics, 553, A6, +doi:10.1051/0004-6361/201219058. + +Ciddor, P. E. (1996), Refractive index of air: new equations for the visible +and near infrared, Applied Optics, 35, 1566-1573. +""" +import numpy as np + +C_KMS = 299792.458 + +def _as_float_array(x): + """Return x as a NumPy float array.""" + return np.asarray(x, dtype=float) + + +def doppler_factor(v_kms): + """ + Relativistic Doppler factor for wavelength shifts. + + Positive velocity means redshift: + lambda_shifted = lambda_rest * doppler_factor(v_kms) + """ + beta = float(v_kms) / C_KMS + if abs(beta) >= 1.0: + raise ValueError("Absolute velocity must be smaller than the speed of light.") + return np.sqrt((1.0 + beta) / (1.0 - beta)) + + +def shift_wavelength_velocity(wave, v_kms): + """ + Shift wavelengths by a radial velocity using the relativistic Doppler factor. + + Parameters + ---------- + wave : array-like + Input wavelength array. + v_kms : float + Radial velocity in km/s. Positive values redshift the wavelengths. + + Returns + ------- + ndarray + Shifted wavelength array. + """ + wave = _as_float_array(wave) + return wave * doppler_factor(v_kms) + + + +def resample_flux_with_velocity_shift_observed_grid(wave, flux, rv_kms): + """ + Apply an RV shift to a model already sampled on the observed wavelength grid. + + Convention + ---------- + Positive ``rv_kms`` redshifts template/model features. The returned flux is + evaluated on the original input wavelength grid. This helper is intended for + observed-grid PHOENIX fitting paths and deliberately does not modify the + legacy ``Spyctres.velocity_correction`` convention. + + Notes + ----- + If the rest-frame model is sampled as ``flux(wave_rest)``, then a positive + radial velocity gives ``wave_obs = wave_rest * doppler_factor(rv_kms)``. + Therefore, to return model flux on a fixed observed wavelength grid, we + sample the rest-frame model at ``wave / doppler_factor(rv_kms)``. + """ + from scipy.interpolate import interp1d + + wave = _as_float_array(wave) + flux = _as_float_array(flux) + + if wave.shape != flux.shape: + raise ValueError("wave and flux must have the same shape.") + + if wave.ndim != 1: + raise ValueError("wave and flux must be 1D arrays.") + + good = np.isfinite(wave) & np.isfinite(flux) + if np.sum(good) < 2: + return np.full_like(wave, np.nan, dtype=float) + + w = wave[good] + f = flux[good] + + if not np.all(np.diff(w) > 0): + idx = np.argsort(w) + w = w[idx] + f = f[idx] + + sample_wave = wave / doppler_factor(float(rv_kms)) + + interpolator = interp1d( + w, + f, + kind="linear", + bounds_error=False, + fill_value="extrapolate", + ) + return np.asarray(interpolator(sample_wave), dtype=float) + +def _ciddor_factor_from_vacuum_angstrom(wave_vac): + """ + Refractive index factor f = lambda_vac / lambda_air + Uses the Ciddor (1996) relation as quoted by Husser et al. (2013), + valid for lambda > 2000 Angstrom. + """ + wave_vac = _as_float_array(wave_vac) + sigma2 = (1.0e4 / wave_vac) ** 2 + f = ( + 1.0 + + 0.05792105 / (238.0185 - sigma2) + + 0.00167917 / (57.362 - sigma2) + ) + return f + + +def vacuum_to_air_ciddor(wave_vac): + """ + Convert vacuum wavelengths to air wavelengths using the + Ciddor (1996) formula quoted in Husser et al. (2013). + + Parameters + ---------- + wave_vac : array-like + Vacuum wavelengths in Angstrom. + + Returns + ------- + ndarray + Air wavelengths in Angstrom. + """ + wave_vac = _as_float_array(wave_vac) + return wave_vac / _ciddor_factor_from_vacuum_angstrom(wave_vac) + + +def air_to_vacuum_ciddor(wave_air, n_iter=3): + """ + Convert air wavelengths to vacuum wavelengths by inverting + the Ciddor relation iteratively. + + Parameters + ---------- + wave_air : array-like + Air wavelengths in Angstrom. + n_iter : int, optional + Number of fixed-point iterations. Default is 3. + + Returns + ------- + ndarray + Vacuum wavelengths in Angstrom. + """ + wave_air = _as_float_array(wave_air) + wave_vac = wave_air.copy() + for _ in range(int(n_iter)): + wave_vac = wave_air * _ciddor_factor_from_vacuum_angstrom(wave_vac) + return wave_vac + + +def convert_wavelength_medium(wave, from_medium, to_medium): + """ + Convert wavelength medium between 'air', 'vacuum', and 'unknown'. + + Conversions involving 'unknown' are rejected deliberately, because the + wavelength medium must be specified explicitly for reliable spectral fitting. + + Parameters + ---------- + wave : array-like + Input wavelength array in Angstrom. + from_medium : str + 'air', 'vacuum', or 'unknown' + to_medium : str + 'air', 'vacuum', or 'unknown' + + Returns + ------- + ndarray + Converted wavelength array. + + Raises + ------ + ValueError + If the conversion is unsupported or ambiguous. + """ + wave = _as_float_array(wave) + from_medium = str(from_medium).lower() + to_medium = str(to_medium).lower() + + if from_medium == to_medium: + return wave.copy() + + if "unknown" in (from_medium, to_medium): + raise ValueError( + "Cannot convert wavelength medium when from/to is 'unknown'." + ) + + if from_medium == "vacuum" and to_medium == "air": + return vacuum_to_air_ciddor(wave) + + if from_medium == "air" and to_medium == "vacuum": + return air_to_vacuum_ciddor(wave) + + raise ValueError( + "Unsupported wavelength-medium conversion: {0} -> {1}".format( + from_medium, to_medium + ) + ) diff --git a/docs/architecture_phoenix.md b/docs/architecture_phoenix.md new file mode 100644 index 0000000..dcbab57 --- /dev/null +++ b/docs/architecture_phoenix.md @@ -0,0 +1,11 @@ +Spyctres.py: public API and backward compatibility +io.py: SpectrumSegment, SpectrumCollection, readers +preprocessing.py: masks, wavelength corrections, optional normalization +waveutils.py: wavelength/RV utilities +phoenix.py: PHOENIX backend +phoenix_forward.py: forward model construction +fitting.py: generic fitting machinery +recipes.py: science workflows +plotting.py: plotting helpers +scripts/: smoke tests +examples/: user-facing notebooks diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 0000000..0ccc578 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,123 @@ +# Spyctres PHOENIX full-spectrum example + +This directory contains a worked notebook example of PHOENIX-based full-spectrum fitting in Spyctres, using a reduced X-SHOOTER UVB spectrum of Gaia21ccu as the reference dataset. + +The notebook is meant to be a clean first example of the generic PHOENIX fitting workflow. It is not intended to be the final precision analysis for this spectrum, and it is not the full benchmark-validation path used for development testing. + +## What this notebook demonstrates + +The example notebook shows how to: + +1. resolve the local PHOENIX template path from environment or config +2. read a reduced 1D spectrum with `Spyctres.io.read_spectrum` +3. inspect the returned `SpectrumSegment` metadata +4. define Balmer-window fitting segments +5. exclude line-core pixels with a simple mask +6. run a PHOENIX fit for `(Teff, [Fe/H], logg, RV)` +7. reconstruct and plot the fitted model +8. interpret the result as a first model-based spectral classification + +The fitter returns physical parameters rather than a formal MK spectral class label. In practice, the fitted parameters can be used as the basis for parameter-based classification. + +## Reference input data + +This example uses: + +`examples/data/TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_UVB.fits` + +This is a reduced merged 1D X-SHOOTER UVB FITS product. Spyctres reconstructs the wavelength array from the FITS WCS information and reads the associated flux, uncertainty, mask, and metadata through the X-SHOOTER reader in `Spyctres/io.py`. + +## PHOENIX path resolution + +The local PHOENIX directory is resolved in this precedence order: + +1. command-line value +2. environment variable `SPYCTRES_PHOENIX_DIR` +3. config file `~/.config/spyctres/config.toml` + +Example config file: + +```toml +[paths] +phoenix_dir = "/path/to/PHOENIXv2" +``` + +If no PHOENIX directory is found, the notebook will stop with an error and ask you to define one of the settings above. + +## What level of result to expect + +This notebook is designed as a first-pass full-spectrum classification example. + +The fitted values should be treated as an initial model-based estimate, not as a final high-precision stellar analysis. Real spectra often require iteration over modelling choices such as: + +- wavelength windows +- line-core masking +- instrumental resolving power +- wavelength medium and velocity conventions +- continuum treatment +-PHOENIX subgrid selection + +In other words, the notebook shows the workflow cleanly, while leaving room for later refinement. + +## Running the example + +A typical workflow is: +1. work from a local editable Spyctres checkout +2. set `SPYCTRES_PHOENIX_DIR` or define `phoenix_dir` in the user config +3. launch Jupyter from the repository +4. open `examples/full_spectrum_classification.ipynb` +5. run the notebook from top to bottom + +## Adapting the example to your own spectrum + +To use your own reduced 1D spectrum, replace the input path in the notebook and, if necessary, choose the appropriate instrument reader in `Spyctres.io.read_spectrum`. + +When adapting the example, you should check: + +- wavelength coverage +- wavelength medium, for example air or vacuum +- wavelength frame, for example barycentric-corrected or not +- resolving power or effective line broadening +- whether the Balmer-window choice is still appropriate +- whether the default line-core mask is sensible for your science case + +## Advanced workflows + +This notebook intentionally stays close to the generic fitting path. +Spyctres also includes a higher-level workflow layer in `Spyctres.recipes` +That module contains more specialized helpers for tasks such as: + +- Balmer-window definitions +- Balmer-line metadata attachment +- sideband-based normalization +- line-core exclusion masks +- model-building and plotting helpers + +Those tools can be useful references when you want a more instrument-specific or more tightly controlled workflow, but they are not required for this first example. + +## Validation reference + +The development validation reference for the Gaia21ccu X-SHOOTER UVB case remains the notebook-faithful smoke test: +```python scripts/xshooter_fit_smoketest.py \ + --preset xshooter_uvb_notebook \ + examples/data/TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_UVB.fits +``` + +That path is useful for regression testing and benchmark comparison. The notebook in this directory is the simpler user-facing worked example. + +## Adding support for another instrument + +Spyctres uses a generic internal spectrum container: `SpectrumSegment` +A new instrument reader should return a `SpectrumSegment` with: +- `wave` +- `flux` +- optional `err` +- default boolean `mask` +- metadata such as `wave_medium`, `wave_frame`, and `resolution_R` where available + +To add a new instrument: +- add a new reader function in `Spyctres/io.py` +- make that function return a `SpectrumSegment` +- register the reader under one or more aliases in the read_spectrum registry + +Instrument-specific I/O belongs in `Spyctres/io.py`, while the fitter itself operates on generic `SpectrumSegment` objects. diff --git a/examples/data/TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_NIR_TELL_CORR.fits b/examples/data/TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_NIR_TELL_CORR.fits new file mode 100644 index 0000000..66b22f6 --- /dev/null +++ b/examples/data/TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_NIR_TELL_CORR.fits @@ -0,0 +1,755 @@ +SIMPLE = T / file does conform to FITS standard BITPIX = -64 / number of bits per data pixel NAXIS = 1 / number of data axes NAXIS1 = 24750 / length of data axis 1 EXTEND = T / FITS dataset may contain extensions COMMENT FITS (Flexible Image Transport System) format is defined in 'AstronomyCOMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H DATE = '2021-12-13T23:02:01' / file creation date (YYYY-MM-DDThh:mm:ss UT) LAMNLIN = 1790 / No. of lines used in wavelength solution LAMRMS = 0.00998969704801667 / RMS of wavelength solution [CUNIT1] CRDER1 = 0.000236116209329022 / Wavelength uncertainty [CUNIT1] CSYER1 = 0.004 / Typical systematic wavelength error [CUNIT1] CUNIT1 = 'nm ' CRPIX1 = 1. CRVAL1 = 994.02 CDELT1 = 0.06 CTYPE1 = 'LINEAR ' ORIGIN = 'ESO ' / European Southern Observatory TELESCOP= 'ESO-VLT-U3' / ESO Telescope Name INSTRUME= 'XSHOOTER' / Instrument used. OBJECT = 'Gaia21ccu' / Original target. RA = 142.961232 / 09:31:50.6 RA (J2000) pointing DEC = -55.29885 / -55:17:55.8 DEC (J2000) pointing EQUINOX = 2000. / Standard FK5 RADECSYS= 'FK5 ' / Coordinate reference frame EXPTIME = 300. / Integration time MJD-OBS = 59558.29567811 / Obs start 2021-12-10T07:05:46.588 DATE-OBS= '2021-12-10T07:05:46.5884' / Observing date UTC = 25544. / 07:05:44.000 UTC at start LST = 27662.806 / 07:41:02.806 LST at start PI-COI = 'UNKNOWN ' / PI-COI name. OBSERVER= 'UNKNOWN ' / Name of observer. BUNIT = 'erg/s/cm2/Angstrom' EXTNAME = 'FLUX ' HDUCLASS= 'ESO ' / hdu classification HDUDOC = 'DICD ' / hdu reference document HDUVERS = 'DICD V6.0' / hdu reference document version HDUCLAS1= 'IMAGE ' / hdu format classification HDUCLAS2= 'DATA ' / hdu type classification ERRDATA = 'ORD11_ERRS' / name of errs extension QUALDATA= 'ORD11_QUAL' / name of qual extension DATAMD5 = 'e162b76ee9af8ee35cc52969206cb816' / MD5 checksum PIPEFILE= 'SCIENCE_TELLURIC_CORR_TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_NIR.fits' TEXPTIME= 1200. ARCFILE = 'XSHOO.2021-12-10T07:05:46.589.fits' / Archive File Name HIERARCH ESO OBS AIRM = 1.6 / Req. max. airmass HIERARCH ESO OBS AMBI FWHM = 1. / Req. max. seeing HIERARCH ESO OBS AMBI TRANS = '3THN ' / Req. sky transparency HIERARCH ESO OBS AOMODE = 'NO_AO ' / Req. AO mode HIERARCH ESO OBS CONTAINER ID = -999 / Scheduling container ID HIERARCH ESO OBS CONTAINER TYPE = 'U ' / Scheduling container type HIERARCH ESO OBS DID = 'ESO-VLT-DIC.OBS-2.2' / OBS Dictionary HIERARCH ESO OBS EXECTIME = 1710 / Expected execution time HIERARCH ESO OBS GRP = '0 ' / linked blocks HIERARCH ESO OBS ID = 3202014 / Observation block ID HIERARCH ESO OBS MOON DIST = 30 / Req. min. angular dist. from moon HIERARCH ESO OBS MOON FLI = 0.2 / Req. max. fractional lunar illum. HIERARCH ESO OBS NAME = 'TOO_Gaia21ccu' / OB name HIERARCH ESO OBS NTPL = 2 / Number of templates within OB HIERARCH ESO OBS OBSERVER = 'UNKNOWN ' / Observer Name HIERARCH ESO OBS PI-COI ID = 74267 / ESO internal PI-COI ID HIERARCH ESO OBS PI-COI NAME = 'UNKNOWN ' / PI-COI name HIERARCH ESO OBS PROG ID = '108.22JZ.001' / ESO program identification HIERARCH ESO OBS START = '2021-12-10T06:58:31' / OB start time HIERARCH ESO OBS TARG NAME = 'Gaia21ccu' / OB target name HIERARCH ESO OBS TPLNO = 2 / Template number within OB HIERARCH ESO OBS TWILIGHT = 0 / Req. twilight HIERARCH ESO OBS WATERVAPOUR = 30. / Req. water vapour HIERARCH ESO TPL DID = 'ESO-VLT-DIC.TPL-1.9' / Data dictionary for TPL HIERARCH ESO TPL EXPNO = 3 / Exposure number within template HIERARCH ESO TPL ID = 'XSHOOTER_slt_obs_AutoNodOnSlit' / Template signa HIERARCH ESO TPL NAME = 'XSHOOTER observations with AutoNodOnSlit offset' HIERARCH ESO TPL NEXP = 8 / Number of exposures within templat HIERARCH ESO TPL PRESEQ = 'XSHOOTER_obs_AutoNodOnSlit.seq' / Sequencer scri HIERARCH ESO TPL START = '2021-12-10T07:05:08' / TPL start time HIERARCH ESO TPL VERSION = '$Revision: 213208 $' / Version of the template HIERARCH ESO TEL AIRM END = 1.22 / Airmass at end HIERARCH ESO TEL AIRM START = 1.25 / Airmass at start HIERARCH ESO TEL ALT = 53.079 / Alt angle at start HIERARCH ESO TEL AMBI FWHM END = 1.08 / Observatory Seeing queried from AS HIERARCH ESO TEL AMBI FWHM START = 0.87 / Observatory Seeing queried from AS HIERARCH ESO TEL AMBI IRSKY TEMP = -94.8 / Temperature of the IR sky, from ra HIERARCH ESO TEL AMBI IWV END = 1.53 / Integrated Water Vapor HIERARCH ESO TEL AMBI IWV START = 1.45 / Integrated Water Vapor HIERARCH ESO TEL AMBI IWV30D END = 1.69 / IWV at 30deg elev. HIERARCH ESO TEL AMBI IWV30D START = 1.66 / IWV at 30deg elev. HIERARCH ESO TEL AMBI IWV30DSTD END = 0.06 / IWV at 30deg elev. HIERARCH ESO TEL AMBI IWV30DSTD START = 0.06 / IWV at 30deg elev. HIERARCH ESO TEL AMBI IWVSTD END = 0.03 / Standard Deviation of Integrated W HIERARCH ESO TEL AMBI IWVSTD START = 0.01 / Standard Deviation of Integrated W HIERARCH ESO TEL AMBI PRES END = 740.8 / Observatory ambient air pressure q HIERARCH ESO TEL AMBI PRES START = 740.7 / Observatory ambient air pressure q HIERARCH ESO TEL AMBI RHUM = 5.5 / Observatory ambient relative humid HIERARCH ESO TEL AMBI TAU0 = 0.003 / Average coherence time HIERARCH ESO TEL AMBI TEMP = 12.59 / Observatory ambient temperature qu HIERARCH ESO TEL AMBI WINDDIR = 17. / Observatory ambient wind direction HIERARCH ESO TEL AMBI WINDSP = 11.25 / Observatory ambient wind speed que HIERARCH ESO TEL AZ = 333.77 / Az angle at start S=0,W=90 HIERARCH ESO TEL CHOP ST = F / True when chopping is active HIERARCH ESO TEL DATE = '2000-01-01T00:00:00' / TCS installation date HIERARCH ESO TEL DID = 'ESO-VLT-DIC.TCS' / Data dictionary for TEL HIERARCH ESO TEL DOME STATUS = 'FULLY-OPEN' / Dome status HIERARCH ESO TEL FO POS = 0. / Adapter focus position during trac HIERARCH ESO TEL FOCU ID = 'CA ' / Telescope focus station ID HIERARCH ESO TEL FOCU LEN = 108.827 / Focal length HIERARCH ESO TEL FOCU SCALE = 1.894 / Focal scale HIERARCH ESO TEL FOCU VALUE = -0.558 / M2 setting HIERARCH ESO TEL GEOELEV = 2648. / Elevation above sea level (m) HIERARCH ESO TEL GEOLAT = -24.6268 / Tel geo latitute (+=North) HIERARCH ESO TEL GEOLON = -70.4045 / Tel geo longitude (+=East) HIERARCH ESO TEL IA FWHM = 1.02 / Delivered seeing corrected by airm HIERARCH ESO TEL IA FWHMLIN = 1.11 / Delivered seeing on IA detector (l HIERARCH ESO TEL IA FWHMLINOBS = 1.26 / Delivered seeing on IA detector (l HIERARCH ESO TEL ID = 'v 347028' / TCS version number HIERARCH ESO TEL MOON DEC = -12.49852 / -12:29:54.6 DEC (J2000) HIERARCH ESO TEL MOON RA = 342.028433 / 22:48:06.8 RA (J2000) HIERARCH ESO TEL OPER = 'I, Condor' / Telescope Operator HIERARCH ESO TEL PARANG END = -37.599 / Parallactic angle at end HIERARCH ESO TEL PARANG START = -45.015 / Parallactic angle at start HIERARCH ESO TEL TARG ALPHA = 93150.9 / Alpha coordinate for the target HIERARCH ESO TEL TARG COORDTYPE = 'M ' / Coordinate type (M=mean A=apparenHIERARCH ESO TEL TARG DELTA = -551757.23 / Delta coordinate for the target HIERARCH ESO TEL TARG EPOCH = 2000. / Epoch HIERARCH ESO TEL TARG EPOCHSYSTEM = 'J ' / Epoch system (default J=Julian)HIERARCH ESO TEL TARG EQUINOX = 2000. / Equinox HIERARCH ESO TEL TARG PARALLAX = 0. / Parallax HIERARCH ESO TEL TARG PMA = 0. / Proper Motion Alpha HIERARCH ESO TEL TARG PMD = 0. / Proper motion Delta HIERARCH ESO TEL TARG RADVEL = 0. / Radial velocity HIERARCH ESO TEL TH M1 TEMP = 11.81 / M1 superficial temperature HIERARCH ESO TEL TRAK STATUS = 'NORMAL ' / Tracking status HIERARCH ESO INS ADC1 DEC = -552323.76247 / Telescope desclination [deg]. HIERARCH ESO INS ADC1 ENC END = 63932 / Encoder position at end [Enc] HIERARCH ESO INS ADC1 ENC START = 65082 / Encoder position at start [Enc] HIERARCH ESO INS ADC1 END = 310.047 / Position angle at end [deg]. HIERARCH ESO INS ADC1 MODE = 'AUTO ' / ADC mode. HIERARCH ESO INS ADC1 RA = 93232.847481 / Telescope right ascension [deg]. HIERARCH ESO INS ADC1 START = 315.627 / ADC position at start [deg]. HIERARCH ESO INS ADC2 DEC = -552323.76247 / Telescope desclination [deg]. HIERARCH ESO INS ADC2 ENC END = 72109 / Encoder position at end [Enc] HIERARCH ESO INS ADC2 ENC START = 70191 / Encoder position at start [Enc] HIERARCH ESO INS ADC2 END = 349.568 / Position angle at end [deg]. HIERARCH ESO INS ADC2 MODE = 'AUTO ' / ADC mode. HIERARCH ESO INS ADC2 RA = 93232.847481 / Telescope right ascension [deg]. HIERARCH ESO INS ADC2 START = 340.367 / ADC position at start [deg]. HIERARCH ESO INS ADC3 DEC = -552323.76247 / Telescope desclination [deg]. HIERARCH ESO INS ADC3 ENC END = 63456 / Encoder position at end [Enc] HIERARCH ESO INS ADC3 ENC START = 64600 / Encoder position at start [Enc] HIERARCH ESO INS ADC3 END = 307.748 / Position angle at end [deg]. HIERARCH ESO INS ADC3 MODE = 'AUTO ' / ADC mode. HIERARCH ESO INS ADC3 RA = 93232.847481 / Telescope right ascension [deg]. HIERARCH ESO INS ADC3 START = 313.3 / ADC position at start [deg]. HIERARCH ESO INS ADC4 DEC = -552323.76247 / Telescope desclination [deg]. HIERARCH ESO INS ADC4 ENC END = 72397 / Encoder position at end [Enc] HIERARCH ESO INS ADC4 ENC START = 70498 / Encoder position at start [Enc] HIERARCH ESO INS ADC4 END = 351.078 / Position angle at end [deg]. HIERARCH ESO INS ADC4 MODE = 'AUTO ' / ADC mode. HIERARCH ESO INS ADC4 RA = 93232.847481 / Telescope right ascension [deg]. HIERARCH ESO INS ADC4 START = 341.862 / ADC position at start [deg]. HIERARCH ESO INS DATE = '2005-04-01' / Instrument release date (yyyy-mm-d HIERARCH ESO INS DID = 'ESO-VLT-DIC.XSHOOTER_ICS-342537' / Data dictiona HIERARCH ESO INS FOCU1 ENC = 26946 / Absolute position [Enc]. HIERARCH ESO INS FOCU1 POS = 14.3 / Position [C]. HIERARCH ESO INS FOCU2 ENC = 11750 / Absolute position [Enc]. HIERARCH ESO INS FOCU2 POS = 0. / Position [C]. HIERARCH ESO INS ID = 'XSHOOTER/176642' / Instrument ID. HIERARCH ESO INS MIRR1 ID = 'Tel ' / Mirror unique ID. HIERARCH ESO INS MIRR1 NAME = 'Tel ' / Mirror name. HIERARCH ESO INS MIRR1 NO = 1 / Mirror slide position. HIERARCH ESO INS MODE = 'SLITSPEC' / Instrument mode used. HIERARCH ESO INS OPTI2 ID = 'SLOT ' / OPTIi unique ID. HIERARCH ESO INS OPTI2 NAME = 'SLOT ' / OPTIi name. HIERARCH ESO INS OPTI2 NO = 3 / OPTIi slot number. HIERARCH ESO INS OPTI2 TYPE = 'MIRROR ' / OPTIi element. HIERARCH ESO INS OPTI3 ID = 'PS7 ' / OPTIi unique ID. HIERARCH ESO INS OPTI3 NAME = '1.0x11 ' / OPTIi name. HIERARCH ESO INS OPTI3 NO = 7 / OPTIi slot number. HIERARCH ESO INS OPTI3 TYPE = 'SLIT ' / OPTIi element. HIERARCH ESO INS OPTI4 ID = 'PS3 ' / OPTIi unique ID. HIERARCH ESO INS OPTI4 NAME = '0.7x11 ' / OPTIi name. HIERARCH ESO INS OPTI4 NO = 3 / OPTIi slot number. HIERARCH ESO INS OPTI4 TYPE = 'SLIT ' / OPTIi element. HIERARCH ESO INS OPTI5 ID = 'PS12 ' / OPTIi unique ID. HIERARCH ESO INS OPTI5 NAME = '0.6x11 ' / OPTIi name. HIERARCH ESO INS OPTI5 NO = 12 / OPTIi slot number. HIERARCH ESO INS OPTI5 TYPE = 'SLIT ' / OPTIi element. HIERARCH ESO INS PATH = 'SLITSPEC' / Optical path used. HIERARCH ESO INS SENS21 ID = 'CA1F1 ' / sensor ID. HIERARCH ESO INS SENS21 NAME = 'Cab.1 Flow rate' / sensor common name. HIERARCH ESO INS SENS21 VAL = 0. / Cab. 1 Flow rate [l/h] HIERARCH ESO INS SENS60 ID = 'NPM ' / sensor ID. HIERARCH ESO INS SENS60 NAME = 'NIR cryo. P' / sensor common name. HIERARCH ESO INS SENS60 VAL = 999999. / NIR Pressure [mbar] HIERARCH ESO INS SENS81 ID = 'RANGE ' / sensor ID. HIERARCH ESO INS SENS81 NAME = 'Heater Range' / sensor common name. HIERARCH ESO INS SENS81 VAL = 4. / Lake 340 Heater Range HIERARCH ESO INS SENS82 ID = 'HEATR ' / sensor ID. HIERARCH ESO INS SENS82 NAME = 'Heater output' / sensor common name. HIERARCH ESO INS SENS82 VAL = 26.9 / Lake 340 Heater output HIERARCH ESO INS SENSOR4 SWSIM = T / If T, function is software simulat HIERARCH ESO INS SENSOR5 SWSIM = T / If T, function is software simulat HIERARCH ESO INS SHUT1 ID = 'INSH ' / Shutter ID. HIERARCH ESO INS SHUT1 NAME = 'Instrument shutter' / Shutter name. HIERARCH ESO INS SHUT1 ST = T / Shutter open. HIERARCH ESO INS SWSIM = 'NORMAL ' / Software simulation. HIERARCH ESO INS TEMP1 ID = 'TMUC ' / Temperature sensor ID. HIERARCH ESO INS TEMP1 NAME = 'UVB Camera temp.' / Temperature sensor name. HIERARCH ESO INS TEMP1 VAL = 14.04 / UVB Camera temp. [C] HIERARCH ESO INS TEMP10 ID = 'TMA ' / Temperature sensor ID. HIERARCH ESO INS TEMP10 NAME = 'Ambient temp.' / Temperature sensor name. HIERARCH ESO INS TEMP10 VAL = 13.57 / Ambient temp. [C] HIERARCH ESO INS TEMP11 ID = 'TMADC ' / Temperature sensor ID. HIERARCH ESO INS TEMP11 NAME = 'BB UVB ADC temp.' / Temperature sensor name. HIERARCH ESO INS TEMP11 VAL = 16.48 / BB UVB ADC temp. [C] HIERARCH ESO INS TEMP2 ID = 'TMUP ' / Temperature sensor ID. HIERARCH ESO INS TEMP2 NAME = 'UVB Prism temp.' / Temperature sensor name. HIERARCH ESO INS TEMP2 VAL = 14.04 / UVB Prism temp. [C] HIERARCH ESO INS TEMP21 ID = 'CA1OT ' / Temperature sensor ID. HIERARCH ESO INS TEMP21 NAME = 'Cab.1 outlet temp.' / Temperature sensor name. HIERARCH ESO INS TEMP21 VAL = 0. / Cab. 1 outlet temp. [C] HIERARCH ESO INS TEMP22 ID = 'CA1IT ' / Temperature sensor ID. HIERARCH ESO INS TEMP22 NAME = 'Cab.1 inlet temp.' / Temperature sensor name. HIERARCH ESO INS TEMP22 VAL = 0. / Cab. 1 inlet temp. [C] HIERARCH ESO INS TEMP23 ID = 'CA1CT ' / Temperature sensor ID. HIERARCH ESO INS TEMP23 NAME = 'Cab.1 temp.' / Temperature sensor name. HIERARCH ESO INS TEMP23 VAL = 0. / Cab. 1 temp. [C] HIERARCH ESO INS TEMP24 ID = 'CA1AT ' / Temperature sensor ID. HIERARCH ESO INS TEMP24 NAME = 'Cab.1 Amb. temp.' / Temperature sensor name. HIERARCH ESO INS TEMP24 VAL = 0. / Cab. 1 Amb. temp. [C] HIERARCH ESO INS TEMP3 ID = 'TMUB ' / Temperature sensor ID. HIERARCH ESO INS TEMP3 NAME = 'UVB bench temp.' / Temperature sensor name. HIERARCH ESO INS TEMP3 VAL = 14.04 / UVB bench temp. [C] HIERARCH ESO INS TEMP4 ID = 'TMVC ' / Temperature sensor ID. HIERARCH ESO INS TEMP4 NAME = 'VIS Camera temp.' / Temperature sensor name. HIERARCH ESO INS TEMP4 VAL = 13.06 / VIS Camera temp. [C] HIERARCH ESO INS TEMP5 ID = 'TMVP ' / Temperature sensor ID. HIERARCH ESO INS TEMP5 NAME = 'VIS Prism temp.' / Temperature sensor name. HIERARCH ESO INS TEMP5 VAL = 14. / VIS Prism temp. [C] HIERARCH ESO INS TEMP6 ID = 'TMVB ' / Temperature sensor ID. HIERARCH ESO INS TEMP6 NAME = 'VIS bench temp.' / Temperature sensor name. HIERARCH ESO INS TEMP6 VAL = 13.4 / VIS bench temp. [C] HIERARCH ESO INS TEMP7 ID = 'TMUCR ' / Temperature sensor ID. HIERARCH ESO INS TEMP7 NAME = 'UVB cryo. temp.' / Temperature sensor name. HIERARCH ESO INS TEMP7 VAL = 10.32 / UVB cryo. temp. [C] HIERARCH ESO INS TEMP8 ID = 'TMVCR ' / Temperature sensor ID. HIERARCH ESO INS TEMP8 NAME = 'VIS cryo. temp.' / Temperature sensor name. HIERARCH ESO INS TEMP8 VAL = 11.09 / VIS cryo. temp. [C] HIERARCH ESO INS TEMP80 ID = 'OBCT ' / Temperature sensor ID. HIERARCH ESO INS TEMP80 NAME = 'OB Cover Tel.' / Temperature sensor name. HIERARCH ESO INS TEMP80 VAL = 103.96 / OB Cover Telescope [K] HIERARCH ESO INS TEMP81 ID = 'OBCB ' / Temperature sensor ID. HIERARCH ESO INS TEMP81 NAME = 'OB Cover Tank' / Temperature sensor name. HIERARCH ESO INS TEMP81 VAL = 103.87 / OB Cover Tank [K] HIERARCH ESO INS TEMP82 ID = 'OBPR ' / Temperature sensor ID. HIERARCH ESO INS TEMP82 NAME = 'OB Prism1' / Temperature sensor name. HIERARCH ESO INS TEMP82 VAL = 103.29 / OB Prism1 [K] HIERARCH ESO INS TEMP83 ID = 'OBCL ' / Temperature sensor ID. HIERARCH ESO INS TEMP83 NAME = 'OB Corrector Lens' / Temperature sensor name. HIERARCH ESO INS TEMP83 VAL = 104.04 / OB Corrector Lens [K] HIERARCH ESO INS TEMP84 ID = 'CP ' / Temperature sensor ID. HIERARCH ESO INS TEMP84 NAME = 'Cryo. Cold Plate' / Temperature sensor name. HIERARCH ESO INS TEMP84 VAL = 86.35 / Cryostat Cold Plate [K] HIERARCH ESO INS TEMP85 ID = 'RS ' / Temperature sensor ID. HIERARCH ESO INS TEMP85 NAME = 'Cryo. rad. shield' / Temperature sensor name. HIERARCH ESO INS TEMP85 VAL = 107.77 / Cryostat rad. shield [K] HIERARCH ESO INS TEMP86 ID = 'DCB ' / Temperature sensor ID. HIERARCH ESO INS TEMP86 NAME = 'CCD Copper bar' / Temperature sensor name. HIERARCH ESO INS TEMP86 VAL = 77.175 / CCD Copper bar [K] HIERARCH ESO INS TEMP9 ID = 'TMNCR ' / Temperature sensor ID. HIERARCH ESO INS TEMP9 NAME = 'NIR cryo. temp.' / Temperature sensor name. HIERARCH ESO INS TEMP9 VAL = 14.56 / NIR cryo. temp. [C] HIERARCH ESO INS TEMP90 ID = 'NHT ' / Temperature sensor ID. HIERARCH ESO INS TEMP90 NAME = 'NIR Head temp.' / Temperature sensor name. HIERARCH ESO INS TEMP90 VAL = 79.001 / NIR Head temp. [K] HIERARCH ESO INS TILT1 AFCX = 1.058 / Flexure corr. in X [volt]. HIERARCH ESO INS TILT1 AFCY = 0.482 / Flexure corr. in Y [volt]. HIERARCH ESO INS TILT1 CONVX = 0.208 / VoltToMarcsec conversion in X. HIERARCH ESO INS TILT1 CONVY = 0.269 / VoltToMarcsec conversion in Y. HIERARCH ESO INS TILT1 ENGX = 6.184 / Piezo voltage at end in X [volt]. HIERARCH ESO INS TILT1 ENGY = 4.376 / Piezo voltage at end in Y [volt]. HIERARCH ESO INS TILT1 ID = 'afcs1 ' / AFCS ID. HIERARCH ESO INS TILT1 MODE = 'AUTO ' / AFCS mode. HIERARCH ESO INS TILT1 NAME = 'Flexure comp. UVB' / AFCS description. HIERARCH ESO INS TILT1 OFFSETX = 5. / Offset voltage in X [volt]. HIERARCH ESO INS TILT1 OFFSETY = 5. / Offset voltage in Y [volt]. HIERARCH ESO INS TILT1 POSXEND = 1.267 / Piezo position at end [volt]. HIERARCH ESO INS TILT1 POSXSTART = 1.095 / Piezo position at start in X [volt HIERARCH ESO INS TILT1 POSYEND = -0.557 / Piezo position at end [volt]. HIERARCH ESO INS TILT1 POSYSTART = -0.648 / Piezo position at start in Y [volt HIERARCH ESO INS TILT1 REFRACXEND = 1.299 / Atm. refr. corr. end in X [volt]. HIERARCH ESO INS TILT1 REFRACXST = 0.998 / Atm. refr. corr. start in X [volt] HIERARCH ESO INS TILT1 REFRACYEND = -1.64 / Atm. refr. corr. end in Y [volt]. HIERARCH ESO INS TILT1 REFRACYST = -1.652 / Atm. refr. corr. start in Y [volt] HIERARCH ESO INS TILT2 AFCX = 0.316 / Flexure corr. in X [volt]. HIERARCH ESO INS TILT2 AFCY = 0.17 / Flexure corr. in Y [volt]. HIERARCH ESO INS TILT2 CONVX = 0.291 / VoltToMarcsec conversion in X. HIERARCH ESO INS TILT2 CONVY = 0.384 / VoltToMarcsec conversion in Y. HIERARCH ESO INS TILT2 ENGX = 5.25 / Piezo voltage at end in X [volt]. HIERARCH ESO INS TILT2 ENGY = 6.232 / Piezo voltage at end in Y [volt]. HIERARCH ESO INS TILT2 ID = 'afcs2 ' / AFCS ID. HIERARCH ESO INS TILT2 MODE = 'AUTO ' / AFCS mode. HIERARCH ESO INS TILT2 NAME = 'Flexure comp. VIS' / AFCS description. HIERARCH ESO INS TILT2 OFFSETX = 5. / Offset voltage in X [volt]. HIERARCH ESO INS TILT2 OFFSETY = 5. / Offset voltage in Y [volt]. HIERARCH ESO INS TILT2 POSXEND = 0.129 / Piezo position at end [volt]. HIERARCH ESO INS TILT2 POSXSTART = 0.283 / Piezo position at start in X [volt HIERARCH ESO INS TILT2 POSYEND = 1.081 / Piezo position at end [volt]. HIERARCH ESO INS TILT2 POSYSTART = 1.16 / Piezo position at start in Y [volt HIERARCH ESO INS TILT2 REFRACXEND = -1.343 / Atm. refr. corr. end in X [volt]. HIERARCH ESO INS TILT2 REFRACXST = -1.029 / Atm. refr. corr. start in X [volt] HIERARCH ESO INS TILT2 REFRACYEND = 1.675 / Atm. refr. corr. end in Y [volt]. HIERARCH ESO INS TILT2 REFRACYST = 1.69 / Atm. refr. corr. start in Y [volt] HIERARCH ESO INS TILT3 AFCX = 0.997 / Flexure corr. in X [volt]. HIERARCH ESO INS TILT3 AFCY = 0.261 / Flexure corr. in Y [volt]. HIERARCH ESO INS TILT3 CONVX = 0.58 / VoltToMarcsec conversion in X. HIERARCH ESO INS TILT3 CONVY = 0.56 / VoltToMarcsec conversion in Y. HIERARCH ESO INS TILT3 ENGX = 7.303 / Piezo voltage at end in X [volt]. HIERARCH ESO INS TILT3 ENGY = 5.34 / Piezo voltage at end in Y [volt]. HIERARCH ESO INS TILT3 ID = 'afcs3 ' / AFCS ID. HIERARCH ESO INS TILT3 MODE = 'AUTO ' / AFCS mode. HIERARCH ESO INS TILT3 NAME = 'Flexure comp. NIR' / AFCS description. HIERARCH ESO INS TILT3 OFFSETX = 5. / Offset voltage in X [volt]. HIERARCH ESO INS TILT3 OFFSETY = 5. / Offset voltage in Y [volt]. HIERARCH ESO INS TILT3 POSXEND = 2.139 / Piezo position at end [volt]. HIERARCH ESO INS TILT3 POSXSTART = 2.238 / Piezo position at start in X [volt HIERARCH ESO INS TILT3 POSYEND = 0.446 / Piezo position at end [volt]. HIERARCH ESO INS TILT3 POSYSTART = 0.294 / Piezo position at start in Y [volt HIERARCH ESO INS TILT3 REFRACXEND = -1.458 / Atm. refr. corr. end in X [volt]. HIERARCH ESO INS TILT3 REFRACXST = -1.147 / Atm. refr. corr. start in X [volt] HIERARCH ESO INS TILT3 REFRACYEND = 1.792 / Atm. refr. corr. end in Y [volt]. HIERARCH ESO INS TILT3 REFRACYST = 1.806 / Atm. refr. corr. start in Y [volt] HIERARCH ESO DET CHIP GAIN = 2.29 / Gain in e-/ADU HIERARCH ESO DET CHIP ID = 'ESO-Hawaii2RG' / Detector ID HIERARCH ESO DET CHIP NAME = 'Hawaii2RG' / Detector name HIERARCH ESO DET CHIP NX = 2048 / Pixels in X HIERARCH ESO DET CHIP NY = 1100 / Pixels in Y HIERARCH ESO DET CHIP PXSPACE = 1.8E-05 / Pixel-Pixel Spacing HIERARCH ESO DET CHIP RON = 0. / Read-out noise in e HIERARCH ESO DET CHIP TYPE = 'IR ' / The Type of Det Chip HIERARCH ESO DET CHOP FREQ = 0 / Chopping Frequency HIERARCH ESO DET CON OPMODE = 'NORMAL ' / Operational Mode HIERARCH ESO DET DID = 'ESO-VLT-DIC.IRACE-1.48' / Dictionary Name and Re HIERARCH ESO DET DIT = 300. / Integration Time HIERARCH ESO DET DITDELAY = 0. / Pause Between DITs HIERARCH ESO DET EXP NAME = 'XSHOOTER_SLT_OBJ_NIR_344_0003' / Exposure Name HIERARCH ESO DET EXP NO = 24656 / Exposure number HIERARCH ESO DET EXP UTC = '2021-12-10T07:10:51.1204' / File Creation Time HIERARCH ESO DET FILE CUBE ST = F / Data Cube On/Off HIERARCH ESO DET FRAM NO = 1 / Frame number HIERARCH ESO DET FRAM TYPE = 'INT ' / Frame type HIERARCH ESO DET FRAM UTC = '2021-12-10T07:10:49.5391' / Time Recv Frame HIERARCH ESO DET IRACE ADC1 DELAY = 7 / ADC Delay Adjustment HIERARCH ESO DET IRACE ADC1 ENABLE = 1 / Enable ADC Board (0/1) HIERARCH ESO DET IRACE ADC1 FILTER1 = 0 / ADC Filter1 Adjustment HIERARCH ESO DET IRACE ADC1 FILTER2 = 0 / ADC Filter2 Adjustment HIERARCH ESO DET IRACE ADC1 HEADER = 1 / Header of ADC Board HIERARCH ESO DET IRACE ADC1 NAME = 'ADC-RG ' / Name for ADC Board HIERARCH ESO DET IRACE ADC2 DELAY = 7 / ADC Delay Adjustment HIERARCH ESO DET IRACE ADC2 ENABLE = 1 / Enable ADC Board (0/1) HIERARCH ESO DET IRACE ADC2 FILTER1 = 0 / ADC Filter1 Adjustment HIERARCH ESO DET IRACE ADC2 FILTER2 = 0 / ADC Filter2 Adjustment HIERARCH ESO DET IRACE ADC2 HEADER = 1 / Header of ADC Board HIERARCH ESO DET IRACE ADC2 NAME = 'ADC-RG ' / Name for ADC Board HIERARCH ESO DET IRACE SEQCONT = F / Sequencer Continuous Mode HIERARCH ESO DET MINDIT = 299.9412 / Minimum DIT HIERARCH ESO DET MODE NAME = ' ' / DCS Detector Mode HIERARCH ESO DET NCORRS = 4 / Read-Out Mode HIERARCH ESO DET NCORRS NAME = 'NonDest ' / Read-Out Mode Name HIERARCH ESO DET NDIT = 1 / # of Sub-Integrations HIERARCH ESO DET NDITSKIP = 0 / DITs skipped at 1st.INT HIERARCH ESO DET NDSAMPLES = 452 / # of Non-Dest. Samples HIERARCH ESO DET NDSKIP = 1 / Samples skipped per DIT HIERARCH ESO DET RSPEED = 6 / Read-Speed Factor HIERARCH ESO DET RSPEEDADD = 0 / Read-Speed Add HIERARCH ESO DET SATLEVEL = 42000 / Saturation Level HIERARCH ESO DET VOLT1 CLKHI1 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI10 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI11 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI12 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI13 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI14 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI15 = 0. / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI16 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI2 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI3 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI4 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI5 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI6 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI7 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI8 = 3.3 / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHI9 = 0. / Set Value High-Clock HIERARCH ESO DET VOLT1 CLKHINM1 = 'clk1Hi FSYNCB' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM10 = 'clk10Hi MODECTRL1' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM11 = 'clk11Hi MODECTRL2' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM12 = 'clock12Hi HORIWMEN' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM13 = 'clock13Hi VERTWMEN' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM14 = 'clock14Hi CSB' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM15 = 'clock15Hi DATACLK' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM16 = 'clock16Hi DATAIN' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM2 = 'clk2Hi VCLK' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM3 = 'clk3Hi LSYNCB' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM4 = 'clk4Hi HCLK' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM5 = 'clk5Hi READEN' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM6 = 'clk6Hi RESETEN' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM7 = 'clk7Hi MAINRESETB' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM8 = 'clk8Hi BUFDISABLE' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHINM9 = 'clk9Hi FASTENPAD' / Name of High-Clock HIERARCH ESO DET VOLT1 CLKHIT1 = 3.3252 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT10 = 3.3301 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT11 = 3.3398 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT12 = 3.3447 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT13 = 3.3398 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT14 = 3.3252 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT15 = 0.0439 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT16 = 3.3398 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT2 = 3.335 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT3 = 3.3301 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT4 = 3.335 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT5 = 3.3301 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT6 = 3.3252 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT7 = 3.3301 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT8 = 3.3252 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKHIT9 = 0.0439 / Tel Value High-Clock HIERARCH ESO DET VOLT1 CLKLO1 = 0. / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO10 = 3.3 / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO11 = 3.3 / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO12 = 0. / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO13 = 0. / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO14 = 0. / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO15 = 0. / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO16 = 3.3 / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO2 = 0. / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO3 = 0. / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO4 = 0. / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO5 = 0. / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO6 = 0. / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO7 = 0. / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO8 = 3.3 / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLO9 = 0. / Set value Low-Clock HIERARCH ESO DET VOLT1 CLKLONM1 = 'clk1Lo FSYNCB' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM10 = 'clk10Lo MODECTRL1' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM11 = 'clk11Lo MODECTRL2' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM12 = 'clock12Lo HORIWMEN' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM13 = 'clock13Lo VERTWMEN' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM14 = 'clock14Lo CSB' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM15 = 'clock15Lo DATACLK' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM16 = 'clock16Lo DATAIN' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM2 = 'clk2Lo VCLK' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM3 = 'clk3Lo LSYNCB' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM4 = 'clk4Lo HCLK' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM5 = 'clk5Lo READEN' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM6 = 'clk6Lo RESETEN' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM7 = 'clk7Lo MAINRESETB' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM8 = 'clk8Lo BUFDISABLE' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLONM9 = 'clk9Lo FASTENPAD' / Name of Low-Clock HIERARCH ESO DET VOLT1 CLKLOT1 = 0.0439 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT10 = 3.3398 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT11 = 3.335 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT12 = 0.0342 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT13 = 0.0537 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT14 = 0.0391 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT15 = 0.0293 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT16 = 3.3398 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT2 = 0.0537 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT3 = 0.0439 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT4 = 0.0488 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT5 = 0.0488 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT6 = 0.0391 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT7 = 0.0488 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT8 = 3.3301 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 CLKLOT9 = 0.0391 / Tel Value Low-Clock HIERARCH ESO DET VOLT1 DC1 = 3.3 / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC10 = 0. / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC11 = 0. / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC12 = 0. / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC13 = 0. / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC14 = 0. / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC15 = 0. / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC2 = 3.3 / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC3 = 3.3 / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC4 = 1.5 / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC5 = 0.5 / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC6 = 2.3 / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC7 = 3.5 / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC8 = 3. / Set value DC-Voltage HIERARCH ESO DET VOLT1 DC9 = 0. / Set value DC-Voltage HIERARCH ESO DET VOLT1 DCNM1 = 'DC1 VDD ' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM10 = 'DC10 ' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM11 = 'DC11 ' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM12 = 'DC12 ' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM13 = 'DC13 ' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM14 = 'DC14 ' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM15 = 'DC15 ' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM2 = 'DC2 VDDA' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM3 = 'DC3 VBIASPOWER' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM4 = 'DC4 DSUB' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM5 = 'DC5 VRESET' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM6 = 'DC6 BIASGATE' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM7 = 'DC7 EXTREF' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM8 = 'DC8 VLOAD' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCNM9 = 'DC9 ' / Name of DC-voltage HIERARCH ESO DET VOLT1 DCTA1 = 3.3105 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA10 = 0.0049 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA11 = 0.0049 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA12 = 0.0049 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA13 = 0.0049 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA14 = 0.0049 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA15 = 0.0049 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA2 = 3.3105 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA3 = 3.3105 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA4 = 1.5088 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA5 = 0.5029 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA6 = 2.3145 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA7 = 3.5107 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA8 = 3.0127 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTA9 = 0.0049 / Tel Value 1 for DC HIERARCH ESO DET VOLT1 DCTB1 = 3.3057 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB10 = 0.0049 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB11 = 0.0049 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB12 = 0.0049 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB13 = 0.0049 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB14 = 0.0049 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB15 = 0.0049 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB2 = 3.3057 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB3 = 3.3008 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB4 = 1.5039 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB5 = 0.5029 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB6 = 2.2656 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB7 = 3.4912 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB8 = 3.0078 / Tel Value 2 for DC HIERARCH ESO DET VOLT1 DCTB9 = 0.0049 / Tel Value 2 for DC HIERARCH ESO DET WIN NX = 2048 / # of Pixels in X HIERARCH ESO DET WIN NY = 1100 / # of Pixels in Y HIERARCH ESO DET WIN STARTX = 1 / Lower left X ref HIERARCH ESO DET WIN STARTY = 1 / Lower left Y ref HIERARCH ESO DET WIN TYPE = 0 / Win-Type: 0=SW/1=HW HIERARCH ESO PRO CATG = 'SCIENCE_TELLURIC_CORR' / Category of pipeline product fHIERARCH ESO PRO B RELOFF RA = 3.502468 HIERARCH ESO PRO B RELOFF DEC = -3.301557 HIERARCH ESO PRO B CUMOFF RA = 1.758225 HIERARCH ESO PRO B CUMOFF DEC = -1.657368 HIERARCH ESO PRO RECT BIN LAMBDA = 0.06 HIERARCH ESO PRO RECT BIN SPACE = 0.21 HIERARCH ESO PRO RECT LAMBDA MIN = 994.02 HIERARCH ESO PRO RECT LAMBDA MAX = 2478.96 HIERARCH ESO PRO RECT SPACE MIN = -10.1300001144409 HIERARCH ESO PRO RECT SPACE MAX = 5.40999984741211 HIERARCH ESO PRO EXTRACT SLIT MIN = 0. HIERARCH ESO PRO EXTRACT SLIT MAX = 0. HIERARCH ESO PRO ANCESTOR = 'XSHOO.2021-12-10T07:05:46.589.fits' HIERARCH ESO PRO DID = 'PRO-1.16' / Data dictionary for PRO HIERARCH ESO PRO TYPE = 'REDUCED ' / Product type HIERARCH ESO PRO TECH = 'ECHELLE,SLIT,NODDING' / Observation technique HIERARCH ESO PRO SCIENCE = T / Scientific product if T HIERARCH ESO PRO REC1 ID = 'xsh_scired_slit_nod' / Pipeline recipe (unique) idenHIERARCH ESO PRO REC1 DRS ID = 'cpl-7.1.4' / Data Reduction System identifier HIERARCH ESO PRO REC1 PIPE ID = 'xshoo/3.5.3' / Pipeline (unique) identifier HIERARCH ESO PRO REC1 RAW1 NAME = 'XSHOO.2021-12-10T07:05:46.589.fits' / File naHIERARCH ESO PRO REC1 RAW1 CATG = 'OBJECT_SLIT_NOD_NIR' / Category of raw frame HIERARCH ESO PRO REC1 RAW2 NAME = 'XSHOO.2021-12-10T07:10:54.742.fits' / File naHIERARCH ESO PRO REC1 RAW2 CATG = 'OBJECT_SLIT_NOD_NIR' / Category of raw frame HIERARCH ESO PRO REC1 RAW3 NAME = 'XSHOO.2021-12-10T07:16:28.169.fits' / File naHIERARCH ESO PRO REC1 RAW3 CATG = 'OBJECT_SLIT_NOD_NIR' / Category of raw frame HIERARCH ESO PRO REC1 RAW4 NAME = 'XSHOO.2021-12-10T07:21:36.985.fits' / File naHIERARCH ESO PRO REC1 RAW4 CATG = 'OBJECT_SLIT_NOD_NIR' / Category of raw frame HIERARCH ESO PRO DATANCOM = 4 / Number of combined frames HIERARCH ESO PRO REC1 CAL1 NAME = 'xsh_paranal_extinct_model_nir.fits' / File naHIERARCH ESO PRO REC1 CAL1 CATG = 'ATMOS_EXT_NIR' / Category of calibration framHIERARCH ESO PRO REC1 CAL1 DATAMD5 = '5a3d3a43ed7161c2aeff04ff6823a0d0' / MD5 siHIERARCH ESO PRO REC1 CAL2 NAME = 'BP_MAP_RP_NIR.fits' / File name of calibratioHIERARCH ESO PRO REC1 CAL2 CATG = 'BP_MAP_RP_NIR' / Category of calibration framHIERARCH ESO PRO REC1 CAL2 DATAMD5 = '6f99f13903272739f959c96eda6d7e1b' / MD5 siHIERARCH ESO PRO REC1 CAL3 NAME = 'DISP_TAB_AFC_NIR.fits' / File name of calibraHIERARCH ESO PRO REC1 CAL3 CATG = 'DISP_TAB_AFC_NIR' / Category of calibration fHIERARCH ESO PRO REC1 CAL3 DATAMD5 = '08116b00f1e628f3dc8cf4a6f0f5c29d' / MD5 siHIERARCH ESO PRO REC1 CAL4 NAME = 'DISP_TAB_NIR.fits' / File name of calibrationHIERARCH ESO PRO REC1 CAL4 CATG = 'DISP_TAB_NIR' / Category of calibration frameHIERARCH ESO PRO REC1 CAL4 DATAMD5 = 'a28bc04667259c06cdc8e6878fd7b30c' / MD5 siHIERARCH ESO PRO REC1 CAL5 NAME = 'MASTER_FLAT_SLIT_NIR.fits' / File name of calHIERARCH ESO PRO REC1 CAL5 CATG = 'MASTER_FLAT_SLIT_NIR' / Category of calibratiHIERARCH ESO PRO REC1 CAL5 DATAMD5 = '7069254589e6c56f9c65c6f10b5b8e29' / MD5 siHIERARCH ESO PRO REC1 CAL6 NAME = 'ORDER_TAB_AFC_SLIT_NIR.fits' / File name of cHIERARCH ESO PRO REC1 CAL6 CATG = 'ORDER_TAB_AFC_SLIT_NIR' / Category of calibraHIERARCH ESO PRO REC1 CAL6 DATAMD5 = '8b8ce5f067aa2d1954586bf5ba159479' / MD5 siHIERARCH ESO PRO REC1 CAL7 NAME = 'ORDER_TAB_EDGES_SLIT_NIR.fits' / File name ofHIERARCH ESO PRO REC1 CAL7 CATG = 'ORDER_TAB_EDGES_SLIT_NIR' / Category of calibHIERARCH ESO PRO REC1 CAL7 DATAMD5 = '128fad785530586a6170e7da94602be6' / MD5 siHIERARCH ESO PRO REC1 CAL8 NAME = 'RESPONSE_MERGE1D_SLIT_NIR.fits' / File name oHIERARCH ESO PRO REC1 CAL8 CATG = 'RESPONSE_MERGE1D_SLIT_NIR' / Category of caliHIERARCH ESO PRO REC1 CAL8 DATAMD5 = 'c37c527fbe8258fe312fcd59a5484b96' / MD5 siHIERARCH ESO PRO REC1 CAL9 NAME = 'M.XSHOOTER.2019-04-04T10:14:08.883.fits' / FiHIERARCH ESO PRO REC1 CAL9 CATG = 'SKY_MAP_NIR' / Category of calibration frame HIERARCH ESO PRO REC1 CAL9 DATAMD5 = '2f73738c78ce0770e44bf543bce2ecc8' / MD5 siHIERARCH ESO PRO REC1 CAL10 NAME = 'SPECTRAL_FORMAT_TAB_NIR.fits' / File name ofHIERARCH ESO PRO REC1 CAL10 CATG = 'SPECTRAL_FORMAT_TAB_NIR' / Category of calibHIERARCH ESO PRO REC1 CAL10 DATAMD5 = '062c1ee8138a40cbb1cb5fcd108f47c1' / MD5 sHIERARCH ESO PRO REC1 CAL11 NAME = 'XSH_MOD_CFG_OPT_2D_NIR.fits' / File name of HIERARCH ESO PRO REC1 CAL11 CATG = 'XSH_MOD_CFG_OPT_2D_NIR' / Category of calibrHIERARCH ESO PRO REC1 CAL11 DATAMD5 = '6985bc998f108a3257aec10ddf258c6a' / MD5 sHIERARCH ESO PRO REC1 CAL12 NAME = 'XSH_MOD_CFG_OPT_AFC_NIR.fits' / File name ofHIERARCH ESO PRO REC1 CAL12 CATG = 'XSH_MOD_CFG_OPT_AFC_NIR' / Category of calibHIERARCH ESO PRO REC1 CAL12 DATAMD5 = '4a4f006155e04fb62a0ee93ed304f480' / MD5 sHIERARCH ESO PRO REC1 PARAM1 NAME = 'keep-temp' / If 'no', temporary files are dHIERARCH ESO PRO REC1 PARAM1 VALUE = 'no ' / Default: 'no' HIERARCH ESO PRO REC1 PARAM2 NAME = 'debug-level' / Additional xshooter debug leHIERARCH ESO PRO REC1 PARAM2 VALUE = 'none ' / Default: 'none' HIERARCH ESO PRO REC1 PARAM3 NAME = 'time-stamp' / Add timestamp to product fileHIERARCH ESO PRO REC1 PARAM3 VALUE = 'false ' / Default: false HIERARCH ESO PRO REC1 PARAM4 NAME = 'decode-bp' / Integer representation of the HIERARCH ESO PRO REC1 PARAM4 VALUE = '1741684735' / Default: 1741684735 HIERARCH ESO PRO REC1 PARAM5 NAME = 'pre-overscan-corr' / pre-overscan correctioHIERARCH ESO PRO REC1 PARAM5 VALUE = '0 ' / Default: 1 HIERARCH ESO PRO REC1 PARAM6 NAME = 'stack-method' / Method used to build masterHIERARCH ESO PRO REC1 PARAM6 VALUE = 'median ' / Default: 'median' HIERARCH ESO PRO REC1 PARAM7 NAME = 'klow ' / Kappa used to clip low level vaHIERARCH ESO PRO REC1 PARAM7 VALUE = '5 ' / Default: 5 HIERARCH ESO PRO REC1 PARAM8 NAME = 'khigh ' / Kappa used to clip high level vHIERARCH ESO PRO REC1 PARAM8 VALUE = '5 ' / Default: 5 HIERARCH ESO PRO REC1 PARAM9 NAME = 'removecrhsingle-sigmalim' / Poisson fluctuaHIERARCH ESO PRO REC1 PARAM9 VALUE = '20 ' / Default: 20 HIERARCH ESO PRO REC1 PARAM10 NAME = 'removecrhsingle-flim' / Minimum contrast bHIERARCH ESO PRO REC1 PARAM10 VALUE = '2 ' / Default: 2 HIERARCH ESO PRO REC1 PARAM11 NAME = 'removecrhsingle-niter' / Max number of iteHIERARCH ESO PRO REC1 PARAM11 VALUE = '4 ' / Default: 4 HIERARCH ESO PRO REC1 PARAM12 NAME = 'rectify-kernel' / Name of the InterpolatioHIERARCH ESO PRO REC1 PARAM12 VALUE = 'tanh ' / Default: 'tanh' HIERARCH ESO PRO REC1 PARAM13 NAME = 'rectify-radius' / Rectify Interpolation raHIERARCH ESO PRO REC1 PARAM13 VALUE = '2 ' / Default: 2 HIERARCH ESO PRO REC1 PARAM14 NAME = 'rectify-bin-lambda' / Wavelength step in tHIERARCH ESO PRO REC1 PARAM14 VALUE = '0.06 ' / Default: -1 HIERARCH ESO PRO REC1 PARAM15 NAME = 'rectify-bin-slit' / Spatial step along theHIERARCH ESO PRO REC1 PARAM15 VALUE = '0.21 ' / Default: -1 HIERARCH ESO PRO REC1 PARAM16 NAME = 'rectify-fast' / Fast if TRUE (Rect[B-A] = HIERARCH ESO PRO REC1 PARAM16 VALUE = 'true ' / Default: true HIERARCH ESO PRO REC1 PARAM17 NAME = 'localize-method' / Localization method (MAHIERARCH ESO PRO REC1 PARAM17 VALUE = 'MANUAL ' / Default: 'MANUAL' HIERARCH ESO PRO REC1 PARAM18 NAME = 'localize-chunk-nb' / Number of chunks in tHIERARCH ESO PRO REC1 PARAM18 VALUE = '10 ' / Default: 10 HIERARCH ESO PRO REC1 PARAM19 NAME = 'localize-thresh' / Threshold relative to tHIERARCH ESO PRO REC1 PARAM19 VALUE = '0.1 ' / Default: 0.1 HIERARCH ESO PRO REC1 PARAM20 NAME = 'localize-deg-lambda' / Degree in lambda inHIERARCH ESO PRO REC1 PARAM20 VALUE = '0 ' / Default: 0 HIERARCH ESO PRO REC1 PARAM21 NAME = 'localize-slit-position' / Object position HIERARCH ESO PRO REC1 PARAM21 VALUE = '0 ' / Default: 0 HIERARCH ESO PRO REC1 PARAM22 NAME = 'localize-slit-hheight' / Object half heighHIERARCH ESO PRO REC1 PARAM22 VALUE = '2 ' / Default: 2 HIERARCH ESO PRO REC1 PARAM23 NAME = 'localize-kappa' / Kappa value for sigma clHIERARCH ESO PRO REC1 PARAM23 VALUE = '3 ' / Default: 3 HIERARCH ESO PRO REC1 PARAM24 NAME = 'localize-niter' / Number of iterations forHIERARCH ESO PRO REC1 PARAM24 VALUE = '3 ' / Default: 3 HIERARCH ESO PRO REC1 PARAM25 NAME = 'localize-use-skymask' / TRUE if we want toHIERARCH ESO PRO REC1 PARAM25 VALUE = 'false ' / Default: false HIERARCH ESO PRO REC1 PARAM26 NAME = 'localize-nod-throw' / Step (arcsec) betweeHIERARCH ESO PRO REC1 PARAM26 VALUE = '0 ' / Default: 0 HIERARCH ESO PRO REC1 PARAM27 NAME = 'extract-method' / Method used for extractiHIERARCH ESO PRO REC1 PARAM27 VALUE = 'NOD ' / Default: 'NOD' HIERARCH ESO PRO REC1 PARAM28 NAME = 'stdextract-interp-hsize' / Half size of maHIERARCH ESO PRO REC1 PARAM28 VALUE = '30 ' / Default: 30 HIERARCH ESO PRO REC1 PARAM29 NAME = 'combinenod-throwlist' / Name of ascii fileHIERARCH ESO PRO REC1 PARAM29 VALUE = 'throwlist.asc' / Default: 'throwlist.asc'HIERARCH ESO PRO REC1 PARAM30 NAME = 'combinenod-method' / Combination method foHIERARCH ESO PRO REC1 PARAM30 VALUE = 'MEAN ' / Default: 'MEAN' HIERARCH ESO PRO REC1 PARAM31 NAME = 'max-slit' / Lower Slit Limit (localize andHIERARCH ESO PRO REC1 PARAM31 VALUE = '5.7 ' / Default: 5.7 HIERARCH ESO PRO REC1 PARAM32 NAME = 'min-slit' / Upper Slit Limit (localize andHIERARCH ESO PRO REC1 PARAM32 VALUE = '-5.3 ' / Default: -5.3 HIERARCH ESO PRO REC1 PARAM33 NAME = 'correct-sky-by-median' / TRUE if the resamHIERARCH ESO PRO REC1 PARAM33 VALUE = 'true ' / Default: true HIERARCH ESO PRO REC1 PARAM34 NAME = 'cut-uvb-spectrum' / TRUE if recipe cuts thHIERARCH ESO PRO REC1 PARAM34 VALUE = 'true ' / Default: true HIERARCH ESO PRO REC1 PARAM35 NAME = 'generate-SDP-format' / TRUE if additional HIERARCH ESO PRO REC1 PARAM35 VALUE = 'true ' / Default: false HIERARCH ESO PRO REC1 PARAM36 NAME = 'dummy-association-keys' / Sets the number HIERARCH ESO PRO REC1 PARAM36 VALUE = '0 ' / Default: 0 HIERARCH ESO PRO REC1 PARAM37 NAME = 'scale-combine-nod-method' / frame scaling HIERARCH ESO PRO REC1 PARAM37 VALUE = '1 ' / Default: 1 HIERARCH ESO PRO REC2 ID = 'molecfit_correct' / Pipeline recipe (unique) identifHIERARCH ESO PRO REC2 DRS ID = 'cpl-7.1.4' / Data Reduction System identifier HIERARCH ESO PRO REC2 PIPE ID = 'molecfit/4.1.1' / Pipeline (unique) identifier HIERARCH ESO PRO REC2 RAW1 NAME = 'TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_NIR.fits'HIERARCH ESO PRO REC2 RAW1 CATG = 'SCIENCE ' / Category of raw frame HIERARCH ESO PRO REC2 PARAM1 NAME = 'USE_ONLY_INPUT_PRIMARY_DATA' / Value=TRUE iHIERARCH ESO PRO REC2 PARAM1 VALUE = 'true ' / Default: false HIERARCH ESO PRO REC2 PARAM2 NAME = 'USE_DATA_EXTENSION_AS_DFLUX' / Only valid iHIERARCH ESO PRO REC2 PARAM2 VALUE = '1 ' / Default: 0 HIERARCH ESO PRO REC2 PARAM3 NAME = 'USE_DATA_EXTENSION_AS_MASK' / Only valid ifHIERARCH ESO PRO REC2 PARAM3 VALUE = '2 ' / Default: 0 HIERARCH ESO PRO REC2 PARAM4 NAME = 'SUPPRESS_EXTENSION' / Suppress arbitrary fiHIERARCH ESO PRO REC2 PARAM4 VALUE = 'false ' / Default: false HIERARCH ESO PRO REC2 PARAM5 NAME = 'MAPPING_CORRECT' / Mapping 'SCIENCE' - 'TELHIERARCH ESO PRO REC2 PARAM5 VALUE = '1 ' / Default: 'NULL' HIERARCH ESO PRO REC2 PARAM6 NAME = 'CHIP_EXTENSIONS' / Flag that determines if HIERARCH ESO PRO REC2 PARAM6 VALUE = 'false ' / Default: false HIERARCH ESO ADA ABSROT END = 174.98068 / Abs rot angle at exp end HIERARCH ESO ADA ABSROT PPOS = 'POS ' / sign of probe position HIERARCH ESO ADA ABSROT START = 167.57216 / Abs rot angle at exp start HIERARCH ESO ADA GUID DEC = -55.27821 / -55:16:41.5 Guide star DEC J2000 HIERARCH ESO ADA GUID RA = 142.805983 / 09:31:13.4 Guide star RA J2000 HIERARCH ESO ADA GUID STATUS = 'ON ' / Status of autoguider HIERARCH ESO ADA POSANG = 46.69136 / Position angle at start HIERARCH ESO OCS DET3 IMGNAME = 'XSHOOTER_SLT_OBJ_NIR_' / Data File Name. HIERARCH ESO SEQ ARM = 'NIR ' / Instrument Arm HIERARCH ESO SEQ CUMOFF DEC = 1.644188 / Cumulative DEC offset HIERARCH ESO SEQ CUMOFF RA = -1.744243 / Cumulative RA offset HIERARCH ESO SEQ CUMOFF X = 0. / Cumulative offset X (perp. to slit HIERARCH ESO SEQ CUMOFF Y = 2.397027 / Cumulative offset Y (along the sli HIERARCH ESO SEQ JITTER WIDTH = 0.5 / Width of the Jitter box (arcsec) HIERARCH ESO SEQ NOD THROW = 5. / Nodding throw length in arcsec HIERARCH ESO SEQ RELOFF DEC = 1.644188 / List of DEC offsets HIERARCH ESO SEQ RELOFF RA = -1.744243 / List of RA offsets HIERARCH ESO QC NPIXSAT = 2303. / Number of saturated pixels HIERARCH ESO QC FPIXSAT = 0.00106905451574569 / Fraction of saturated pixels HIERARCH ESO QC CRRATE = 6.648769 / Number of detected CRH per cm2 and s HIERARCH ESO QC NCRH = 13922 / Number of detected cosmic ray hits HIERARCH ESO QC NCRH AVG = 13922. / Average number of cosmic ray hits per frame HIERARCH ESO QC NCRH TOT = 27844 / Total number of cosmic ray hits on frames HIERARCH ESO QC VRAD BARYCOR = 13.5224266624434 / Barycentric radial velocity coHIERARCH ESO QC VRAD HELICOR = 13.5196554244168 / Heliocentric radial velocity cCOMMENT FITS (Flexible Image Transport System) format is defined in 'AstronomyCOMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H CHECKSUM= 'SXQLVUOLSUOLSUOL' / HDU checksum updated 2021-12-13T23:02:01 DATASUM = '3482482095' / data unit checksum updated 2021-12-13T23:02:01 END <)?t@ ԥ<\AQ?< +iK<"#&@<\dmh+<И6W<ۈ +p<`Ғ%!<] 4<(xv<.ڠ<<HMd异"J<$Ul<5ҵ<펣(<v 'x)h<#;t><톞^Gpa<)1< G!w<h&<9j+;<G b<} ~lj|KGN}<ƍǺ<#+<쭡 +,N> nS< Zip<<9u(mL<4i$?<2D<랈<5!A<&l,<)A<%F<|"}<d4H<߲DN<&kfw.%< 1~(<0'LO<6><…v<<z<ꃃ >(_?< x<#q<,K<:ul ~<-d~w<⾄]wU\#. ?S<51Ed<[<҇R<;<܅ctr !"Q< ?] ><b<Os <NW<cd<S?<.@lCH<꧜-B<=]<[i$"y/<|q<3o<3<_<)V<雚t<a;d<{K<^{iK<W`<*<\<襲bCZQ<g <圀hٹ9rJ<VY6<ěĆ<+|.0<誎}c<͓T]<.ժ{< (FH< P<7p<7M<zNx<3ʒ5<څR<0h<Բ(ݮ.< <-j2<= FG(E.<>6<6^E<n<?'^I<1YH^< +M<ڞ\ +<bc<.5&<*X/C6<2e(ec3@<2<E<x<5*&ӛ<8h<6gAX<΋v<d k]< 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{'Ų'ɇ'sL'ˬy'^D'?'N'QI'<''X'l'7'D''˃'''='C'ˍ'͔'I'j'ts'IE'M'''t'@'Щ'''Ι'иN'X'VI'ͫj''@'{''̬'+'ϋ<''̋''˚'&'=''c'*S'͕'Џ')'щ'E'('Z' '3'ш''s's'+'C'V''-E'"XTENSION= 'IMAGE ' / IMAGE extension BITPIX = -32 / number of bits per data pixel NAXIS = 1 / number of data axes NAXIS1 = 12854 / length of data axis 1 PCOUNT = 0 / required keyword; must = 0 GCOUNT = 1 / required keyword; must = 1 EXTNAME = 'ERRS ' CRPIX1 = 1. CRVAL1 = 298.92 CDELT1 = 0.02 CTYPE1 = 'LINEAR ' BUNIT = 'erg/s/cm2/Angstrom' HDUCLASS= 'ESO ' / hdu classification HDUDOC = 'DICD ' / hdu reference document HDUVERS = 'DICD V6.0' / hdu reference document version HDUCLAS1= 'IMAGE ' / hdu format classification HDUCLAS2= 'DATA ' / hdu type classification HDUCLAS3= 'RMSE ' / hdu info classification SCIDATA = 'FLUX ' / name of data extension QUALDATA= 'QUAL ' / name of qual extension CUNIT1 = 'nm ' CHECKSUM= 'KJCnNICnKICnKICn' / HDU checksum updated 2021-12-13T16:58:09 DATASUM = '1912114866' / data unit checksum updated 2021-12-13T16:58:09 END &s&&f&M!&d&l&l&&j&W&&&&b&%&,&&i&c&e&y*&&a&N&l&oy&0&O&P&ĺ&T&&|&,&&X&ƞy&d&/&'y&Y&ɢ&;&ǒ&Y&ÞS&[&&.l&&&e):t)X)))) )l)w)r))Wd)))ߤ))Z))W)F)]c)()N)D)M)+)x){'r)r)t.w)p4)l{)hs)c>)`)_)a=")Zz)Wy)Y I)T )L)G)P^)F$)Ej)@k%)>1f);K);)9.c)5F)1vb).)/ ))b)*y)%)")" ) )̊)d)#)\))))) N)o) ))a))wU(()(7((F(q((( (H(( +(((:(Ֆ([0( (W(4(4((/(_((V(+|(t((#((('(((Д(2((p(N(g(2(((s((A(:$((ؗ(((C(ۜ(17(*>(|m(|(x(sa (r5(l(i@(h(c 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$P$Q*$T}J$T$T~$S$S$UO$Vv$VG$X$V`$U\$V,$V$U$U$S$V$W$X$XO$V/$WZ[$Xn$W$XU$[=$Zc$YT$Ya$ZJ$[$Z2$ZD]$Z$$Y$Y$ZŴ$Ze$Z+$\%R$[=^$[Њ$\$\h$]$$]$[$\$]_$\$]up$]$^|$_$^,$]FO$]n$\0$\$$^2E$]s$^=$_`$^f$_$^$]$]!$_$_i/$_V$^$`$_$^#$^/s$^$^6$`$aB$_E$DJ$Cc[$BD$D$C$CB$CT$D$Doi$E{$F$GE$F$F$F=$G$F$Gk$H$G$F\$G$G$Hn$GA$G$$H$$H6$IV$J$HF$H@$H}$Iݚ$J?~$H '$GH$H$HD$H=!$I<$IN$H$Hv$E$E $F$E$F$K +$J8$Ic$J$K$LcJ$LFG$L$N$N$O$Pk$O$O$Oj$N$NK$Mn$N*$O$R$P$Oz$O$O3$P$P]$P6$P$P/$O $O$M`$Lі$M$Nm$O$QU$R*$R9v$RN$Q$Ou$N:$Mt$Mi$Nv$Ok$N$LYV$K7$Lbq$M4C$M,$N$P$P4$Q:$R+$Q$Q6$Q͊$Q$SI$S$T$U$T$U+$U$Uz$U$V{$VZ$Vq$X)$W8$VSN$W?$Ug`$V]j$W$Wܱ$X $X5$V$W|$W$W3$X@D$X($X$Xg$Wa$W$W$W^$Vz$Xu$Z%$W$W$$U$U_$Wa$Ys$Y $Z$Zh$Y5$ZgB$W$VdU$X$Yf$Y0$Zx$ZH$[V$[$[SF$Y$Z$Z$[$[Wr$Z$YQ$Y/$Y;$Y$[$[;$Z$Y$ZE$[„$\g$\$\VJ$]?o$^VN$]gX$[$[]$ZQ3$Y}$Y4"$Y%$Z=$Y2y$Y/$YXU$X$YE$Y]$Y֧$Zo2$Z$Y$Z5e$ZC%$Y$Y$Z'$ZՂ$\xI$[$[]$\)$\{$]A.$^I$_U$^$_M$_HL$_O$^ǿ$^6$]Vt$]yV$]$[2$Y$YS$Y$[Y$Z$Z$\$\$]xj$adt$_!J$^$`q0$_{$^8$]ݮ$^:$]$\$]$]S$]q$[a$\e$^$`.$`$b,$cO$bK$bI#$ax$`g0$`$`j$$_2$_$^$]`$\$]$[ܙ$[ $]o$]$`$^$_M$_t$`;$0Q$$i$k3$pA$$z$$1$$L$$~$$M$$($c$$$x$$p$R $V$_$$$V$to$ $$!$$aR$$c$$Tb$(`$\$,$n$$5K$'T$~$$;$y,$r$1B$$$i$`$$h$g$$$ٿ$ $F$X$h$9$q$<$$y)$$;$cc$ʹ$2'$@$$$oB$U[$$O$z$y$ $$$$$/$$6$o>$dp$y$$p$zW$?$ܽ$t$l$/ $k$$$%$M$}Z$$?$N$?$PC$#$$$=t$U$$P$G $$[D$A$$0$4$~$P$w$$y$1'$$ +$K$*$$X$$$p<$7$$s$24$$x $$ $$ 9$4$E$$=$n$8$$T$$ʛ$:$T8$B$$${$AD$ $ $n $_$c$> +$l$$x$ޡ$~r$X7$v $${?$@z$$g$|$$x$b $;$$$`$XTENSION= 'IMAGE ' / IMAGE extension BITPIX = 32 / number of bits per data pixel NAXIS = 1 / number of data axes NAXIS1 = 12854 / length of data axis 1 PCOUNT = 0 / required keyword; must = 0 GCOUNT = 1 / required keyword; must = 1 EXTNAME = 'QUAL ' HDUCLASS= 'ESO ' / hdu classification HDUDOC = 'DICD ' / hdu reference document HDUVERS = 'DICD V6.0' / hdu reference document version HDUCLAS1= 'IMAGE ' / hdu format classification HDUCLAS2= 'DATA ' / hdu type classification HDUCLAS3= 'RMSE ' / hdu info classification SCIDATA = 'FLUX ' / name of data extension ERRDATA = 'ERRS ' / name of errs extension CRPIX1 = 1. CRVAL1 = 298.92 CDELT1 = 0.02 CTYPE1 = 'LINEAR ' CUNIT1 = 'nm ' BUNIT = ' ' CHECKSUM= 'NkVTNjVRNjVRNjVR' / HDU checksum updated 2021-12-13T16:58:09 DATASUM = '514850816' / data unit checksum updated 2021-12-13T16:58:09 END @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ \ No newline at end of file diff --git a/examples/data/TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_VIS_TELL_CORR.fits b/examples/data/TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_VIS_TELL_CORR.fits new file mode 100644 index 0000000..2bff688 --- /dev/null +++ b/examples/data/TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_VIS_TELL_CORR.fits @@ -0,0 +1,712 @@ +SIMPLE = T / file does conform to FITS standard BITPIX = -64 / number of bits per data pixel NAXIS = 1 / number of data axes NAXIS1 = 24318 / length of data axis 1 EXTEND = T / FITS dataset may contain extensions COMMENT FITS (Flexible Image Transport System) format is defined in 'AstronomyCOMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H DATE = '2021-12-13T23:42:20' / file creation date (YYYY-MM-DDThh:mm:ss UT) LAMNLIN = 4147 / No. of lines used in wavelength solution LAMRMS = 0.00313827741249968 / RMS of wavelength solution [CUNIT1] CRDER1 = 4.87331308395932E-05 / Wavelength uncertainty [CUNIT1] CSYER1 = 0.02 / Typical systematic wavelength error [CUNIT1] CUNIT1 = 'nm ' CRPIX1 = 1. CRVAL1 = 533.66 CDELT1 = 0.02 CTYPE1 = 'LINEAR ' ORIGIN = 'ESO ' / European Southern Observatory TELESCOP= 'ESO-VLT-U3' / ESO Telescope Name INSTRUME= 'XSHOOTER' / Instrument used. OBJECT = 'Gaia21ccu' / Original target. RA = 142.961232 / 09:31:50.6 RA (J2000) pointing DEC = -55.29885 / -55:17:55.8 DEC (J2000) pointing EQUINOX = 2000. / Standard FK5 RADECSYS= 'FK5 ' / Coordinate reference frame EXPTIME = 220. / Total integration time MJD-OBS = 59558.29564211 / MJD start (2021-12-10T07:05:43.478) DATE-OBS= '2021-12-10T07:05:43.478' / Date of observation UTC = 25539. / 07:05:39.000 UTC at start LST = 27657.792 / 07:40:57.792 LST at start PI-COI = 'UNKNOWN ' / PI-COI name. OBSERVER= 'UNKNOWN ' / Name of observer. INHERIT = T / denotes the INHERIT keyword convention BUNIT = 'erg/s/cm2/Angstrom' EXTNAME = 'FLUX ' HDUCLASS= 'ESO ' / hdu classification HDUDOC = 'DICD ' / hdu reference document HDUVERS = 'DICD V6.0' / hdu reference document version HDUCLAS1= 'IMAGE ' / hdu format classification HDUCLAS2= 'DATA ' / hdu type classification ERRDATA = 'ORD16_ERRS' / name of errs extension QUALDATA= 'ORD16_QUAL' / name of qual extension DATAMD5 = '30d0b1f93f5aa871160de46dffb90a59' / MD5 checksum PIPEFILE= 'SCIENCE_TELLURIC_CORR_TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_VIS.fits' TEXPTIME= 440. ARCFILE = 'XSHOO.2021-12-10T07:05:43.478.fits' / Archive File Name HIERARCH ESO OBS AIRM = 1.6 / Req. max. airmass HIERARCH ESO OBS AMBI FWHM = 1. / Req. max. seeing HIERARCH ESO OBS AMBI TRANS = '3THN ' / Req. sky transparency HIERARCH ESO OBS AOMODE = 'NO_AO ' / Req. AO mode HIERARCH ESO OBS CONTAINER ID = -999 / Scheduling container ID HIERARCH ESO OBS CONTAINER TYPE = 'U ' / Scheduling container type HIERARCH ESO OBS DID = 'ESO-VLT-DIC.OBS-2.2' / OBS Dictionary HIERARCH ESO OBS EXECTIME = 1710 / Expected execution time HIERARCH ESO OBS GRP = '0 ' / linked blocks HIERARCH ESO OBS ID = 3202014 / Observation block ID HIERARCH ESO OBS MOON DIST = 30 / Req. min. angular dist. from moon HIERARCH ESO OBS MOON FLI = 0.2 / Req. max. fractional lunar illum. HIERARCH ESO OBS NAME = 'TOO_Gaia21ccu' / OB name HIERARCH ESO OBS NTPL = 2 / Number of templates within OB HIERARCH ESO OBS OBSERVER = 'UNKNOWN ' / Observer Name HIERARCH ESO OBS PI-COI ID = 74267 / ESO internal PI-COI ID HIERARCH ESO OBS PI-COI NAME = 'UNKNOWN ' / PI-COI name HIERARCH ESO OBS PROG ID = '108.22JZ.001' / ESO program identification HIERARCH ESO OBS START = '2021-12-10T06:58:31' / OB start time HIERARCH ESO OBS TARG NAME = 'Gaia21ccu' / OB target name HIERARCH ESO OBS TPLNO = 2 / Template number within OB HIERARCH ESO OBS TWILIGHT = 0 / Req. twilight HIERARCH ESO OBS WATERVAPOUR = 30. / Req. water vapour HIERARCH ESO TPL DID = 'ESO-VLT-DIC.TPL-1.9' / Data dictionary for TPL HIERARCH ESO TPL EXPNO = 2 / Exposure number within template HIERARCH ESO TPL ID = 'XSHOOTER_slt_obs_AutoNodOnSlit' / Template signa HIERARCH ESO TPL NAME = 'XSHOOTER observations with AutoNodOnSlit offset' HIERARCH ESO TPL NEXP = 8 / Number of exposures within templat HIERARCH ESO TPL PRESEQ = 'XSHOOTER_obs_AutoNodOnSlit.seq' / Sequencer scri HIERARCH ESO TPL START = '2021-12-10T07:05:08' / TPL start time HIERARCH ESO TPL VERSION = '$Revision: 213208 $' / Version of the template HIERARCH ESO TEL AIRM END = 1.226 / Airmass at end HIERARCH ESO TEL AIRM START = 1.25 / Airmass at start HIERARCH ESO TEL ALT = 53.071 / Alt angle at start HIERARCH ESO TEL AMBI FWHM END = 1.07 / Observatory Seeing queried from AS HIERARCH ESO TEL AMBI FWHM START = 0.87 / Observatory Seeing queried from AS HIERARCH ESO TEL AMBI IRSKY TEMP = -94.8 / Temperature of the IR sky, from ra HIERARCH ESO TEL AMBI IWV END = 1.53 / Integrated Water Vapor HIERARCH ESO TEL AMBI IWV START = 1.45 / Integrated Water Vapor HIERARCH ESO TEL AMBI IWV30D END = 1.69 / IWV at 30deg elev. HIERARCH ESO TEL AMBI IWV30D START = 1.66 / IWV at 30deg elev. HIERARCH ESO TEL AMBI IWV30DSTD END = 0.06 / IWV at 30deg elev. HIERARCH ESO TEL AMBI IWV30DSTD START = 0.06 / IWV at 30deg elev. HIERARCH ESO TEL AMBI IWVSTD END = 0.03 / Standard Deviation of Integrated W HIERARCH ESO TEL AMBI IWVSTD START = 0.01 / Standard Deviation of Integrated W HIERARCH ESO TEL AMBI PRES END = 740.7 / Observatory ambient air pressure q HIERARCH ESO TEL AMBI PRES START = 740.7 / Observatory ambient air pressure q HIERARCH ESO TEL AMBI RHUM = 5.5 / Observatory ambient relative humid HIERARCH ESO TEL AMBI TAU0 = 0.003 / Average coherence time HIERARCH ESO TEL AMBI TEMP = 12.59 / Observatory ambient temperature qu HIERARCH ESO TEL AMBI WINDDIR = 17. / Observatory ambient wind direction HIERARCH ESO TEL AMBI WINDSP = 11.25 / Observatory ambient wind speed que HIERARCH ESO TEL AZ = 333.756 / Az angle at start S=0,W=90 HIERARCH ESO TEL CHOP ST = F / True when chopping is active HIERARCH ESO TEL DATE = '2000-01-01T00:00:00' / TCS installation date HIERARCH ESO TEL DID = 'ESO-VLT-DIC.TCS' / Data dictionary for TEL HIERARCH ESO TEL DOME STATUS = 'FULLY-OPEN' / Dome status HIERARCH ESO TEL FO POS = 0. / Adapter focus position during trac HIERARCH ESO TEL FOCU ID = 'CA ' / Telescope focus station ID HIERARCH ESO TEL FOCU LEN = 108.827 / Focal length HIERARCH ESO TEL FOCU SCALE = 1.894 / Focal scale HIERARCH ESO TEL FOCU VALUE = -0.558 / M2 setting HIERARCH ESO TEL GEOELEV = 2648. / Elevation above sea level (m) HIERARCH ESO TEL GEOLAT = -24.6268 / Tel geo latitute (+=North) HIERARCH ESO TEL GEOLON = -70.4045 / Tel geo longitude (+=East) HIERARCH ESO TEL IA FWHM = 1.02 / Delivered seeing corrected by airm HIERARCH ESO TEL IA FWHMLIN = 1.1 / Delivered seeing on IA detector (l HIERARCH ESO TEL IA FWHMLINOBS = 1.26 / Delivered seeing on IA detector (l HIERARCH ESO TEL ID = 'v 347028' / TCS version number HIERARCH ESO TEL MOON DEC = -12.49852 / -12:29:54.6 DEC (J2000) HIERARCH ESO TEL MOON RA = 342.028433 / 22:48:06.8 RA (J2000) HIERARCH ESO TEL OPER = 'I, Condor' / Telescope Operator HIERARCH ESO TEL PARANG END = -39.444 / Parallactic angle at end HIERARCH ESO TEL PARANG START = -45.038 / Parallactic angle at start HIERARCH ESO TEL TARG ALPHA = 93150.9 / Alpha coordinate for the target HIERARCH ESO TEL TARG COORDTYPE = 'M ' / Coordinate type (M=mean A=apparenHIERARCH ESO TEL TARG DELTA = -551757.23 / Delta coordinate for the target HIERARCH ESO TEL TARG EPOCH = 2000. / Epoch HIERARCH ESO TEL TARG EPOCHSYSTEM = 'J ' / Epoch system (default J=Julian)HIERARCH ESO TEL TARG EQUINOX = 2000. / Equinox HIERARCH ESO TEL TARG PARALLAX = 0. / Parallax HIERARCH ESO TEL TARG PMA = 0. / Proper Motion Alpha HIERARCH ESO TEL TARG PMD = 0. / Proper motion Delta HIERARCH ESO TEL TARG RADVEL = 0. / Radial velocity HIERARCH ESO TEL TH M1 TEMP = 11.81 / M1 superficial temperature HIERARCH ESO TEL TRAK STATUS = 'NORMAL ' / Tracking status HIERARCH ESO INS ADC1 DEC = -552323.73644 / Telescope desclination [deg]. HIERARCH ESO INS ADC1 ENC END = 64230 / Encoder position at end [Enc] HIERARCH ESO INS ADC1 ENC START = 65082 / Encoder position at start [Enc] HIERARCH ESO INS ADC1 END = 311.493 / Position angle at end [deg]. HIERARCH ESO INS ADC1 MODE = 'AUTO ' / ADC mode. HIERARCH ESO INS ADC1 RA = 93232.844675 / Telescope right ascension [deg]. HIERARCH ESO INS ADC1 START = 315.627 / ADC position at start [deg]. HIERARCH ESO INS ADC2 DEC = -552323.73644 / Telescope desclination [deg]. HIERARCH ESO INS ADC2 ENC END = 71630 / Encoder position at end [Enc] HIERARCH ESO INS ADC2 ENC START = 70191 / Encoder position at start [Enc] HIERARCH ESO INS ADC2 END = 347.35 / Position angle at end [deg]. HIERARCH ESO INS ADC2 MODE = 'AUTO ' / ADC mode. HIERARCH ESO INS ADC2 RA = 93232.844675 / Telescope right ascension [deg]. HIERARCH ESO INS ADC2 START = 340.367 / ADC position at start [deg]. HIERARCH ESO INS ADC3 DEC = -552323.73644 / Telescope desclination [deg]. HIERARCH ESO INS ADC3 ENC END = 63753 / Encoder position at end [Enc] HIERARCH ESO INS ADC3 ENC START = 64600 / Encoder position at start [Enc] HIERARCH ESO INS ADC3 END = 309.189 / Position angle at end [deg]. HIERARCH ESO INS ADC3 MODE = 'AUTO ' / ADC mode. HIERARCH ESO INS ADC3 RA = 93232.844675 / Telescope right ascension [deg]. HIERARCH ESO INS ADC3 START = 313.3 / ADC position at start [deg]. HIERARCH ESO INS ADC4 DEC = -552323.73644 / Telescope desclination [deg]. HIERARCH ESO INS ADC4 ENC END = 71941 / Encoder position at end [Enc] HIERARCH ESO INS ADC4 ENC START = 70498 / Encoder position at start [Enc] HIERARCH ESO INS ADC4 END = 348.865 / Position angle at end [deg]. HIERARCH ESO INS ADC4 MODE = 'AUTO ' / ADC mode. HIERARCH ESO INS ADC4 RA = 93232.844675 / Telescope right ascension [deg]. HIERARCH ESO INS ADC4 START = 341.862 / ADC position at start [deg]. HIERARCH ESO INS DATE = '2005-04-01' / Instrument release date (yyyy-mm-d HIERARCH ESO INS DID = 'ESO-VLT-DIC.XSHOOTER_ICS-342537' / Data dictiona HIERARCH ESO INS FOCU1 ENC = 26946 / Absolute position [Enc]. HIERARCH ESO INS FOCU1 POS = 14.3 / Position [C]. HIERARCH ESO INS FOCU2 ENC = 11750 / Absolute position [Enc]. HIERARCH ESO INS FOCU2 POS = 0. / Position [C]. HIERARCH ESO INS ID = 'XSHOOTER/176642' / Instrument ID. HIERARCH ESO INS MIRR1 ID = 'Tel ' / Mirror unique ID. HIERARCH ESO INS MIRR1 NAME = 'Tel ' / Mirror name. HIERARCH ESO INS MIRR1 NO = 1 / Mirror slide position. HIERARCH ESO INS MODE = 'SLITSPEC' / Instrument mode used. HIERARCH ESO INS OPTI2 ID = 'SLOT ' / OPTIi unique ID. HIERARCH ESO INS OPTI2 NAME = 'SLOT ' / OPTIi name. HIERARCH ESO INS OPTI2 NO = 3 / OPTIi slot number. HIERARCH ESO INS OPTI2 TYPE = 'MIRROR ' / OPTIi element. HIERARCH ESO INS OPTI3 ID = 'PS7 ' / OPTIi unique ID. HIERARCH ESO INS OPTI3 NAME = '1.0x11 ' / OPTIi name. HIERARCH ESO INS OPTI3 NO = 7 / OPTIi slot number. HIERARCH ESO INS OPTI3 TYPE = 'SLIT ' / OPTIi element. HIERARCH ESO INS OPTI4 ID = 'PS3 ' / OPTIi unique ID. HIERARCH ESO INS OPTI4 NAME = '0.7x11 ' / OPTIi name. HIERARCH ESO INS OPTI4 NO = 3 / OPTIi slot number. HIERARCH ESO INS OPTI4 TYPE = 'SLIT ' / OPTIi element. HIERARCH ESO INS OPTI5 ID = 'PS12 ' / OPTIi unique ID. HIERARCH ESO INS OPTI5 NAME = '0.6x11 ' / OPTIi name. HIERARCH ESO INS OPTI5 NO = 12 / OPTIi slot number. HIERARCH ESO INS OPTI5 TYPE = 'SLIT ' / OPTIi element. HIERARCH ESO INS PATH = 'SLITSPEC' / Optical path used. HIERARCH ESO INS SENS21 ID = 'CA1F1 ' / sensor ID. HIERARCH ESO INS SENS21 NAME = 'Cab.1 Flow rate' / sensor common name. HIERARCH ESO INS SENS21 VAL = 0. / Cab. 1 Flow rate [l/h] HIERARCH ESO INS SENS60 ID = 'NPM ' / sensor ID. HIERARCH ESO INS SENS60 NAME = 'NIR cryo. P' / sensor common name. HIERARCH ESO INS SENS60 VAL = 999999. / NIR Pressure [mbar] HIERARCH ESO INS SENS81 ID = 'RANGE ' / sensor ID. HIERARCH ESO INS SENS81 NAME = 'Heater Range' / sensor common name. HIERARCH ESO INS SENS81 VAL = 4. / Lake 340 Heater Range HIERARCH ESO INS SENS82 ID = 'HEATR ' / sensor ID. HIERARCH ESO INS SENS82 NAME = 'Heater output' / sensor common name. HIERARCH ESO INS SENS82 VAL = 27.1 / Lake 340 Heater output HIERARCH ESO INS SENSOR4 SWSIM = T / If T, function is software simulat HIERARCH ESO INS SENSOR5 SWSIM = T / If T, function is software simulat HIERARCH ESO INS SHUT1 ID = 'INSH ' / Shutter ID. HIERARCH ESO INS SHUT1 NAME = 'Instrument shutter' / Shutter name. HIERARCH ESO INS SHUT1 ST = T / Shutter open. HIERARCH ESO INS SWSIM = 'NORMAL ' / Software simulation. HIERARCH ESO INS TEMP1 ID = 'TMUC ' / Temperature sensor ID. HIERARCH ESO INS TEMP1 NAME = 'UVB Camera temp.' / Temperature sensor name. HIERARCH ESO INS TEMP1 VAL = 14.13 / UVB Camera temp. [C] HIERARCH ESO INS TEMP10 ID = 'TMA ' / Temperature sensor ID. HIERARCH ESO INS TEMP10 NAME = 'Ambient temp.' / Temperature sensor name. HIERARCH ESO INS TEMP10 VAL = 13.57 / Ambient temp. [C] HIERARCH ESO INS TEMP11 ID = 'TMADC ' / Temperature sensor ID. HIERARCH ESO INS TEMP11 NAME = 'BB UVB ADC temp.' / Temperature sensor name. HIERARCH ESO INS TEMP11 VAL = 16.48 / BB UVB ADC temp. [C] HIERARCH ESO INS TEMP2 ID = 'TMUP ' / Temperature sensor ID. HIERARCH ESO INS TEMP2 NAME = 'UVB Prism temp.' / Temperature sensor name. HIERARCH ESO INS TEMP2 VAL = 14.04 / UVB Prism temp. [C] HIERARCH ESO INS TEMP21 ID = 'CA1OT ' / Temperature sensor ID. HIERARCH ESO INS TEMP21 NAME = 'Cab.1 outlet temp.' / Temperature sensor name. HIERARCH ESO INS TEMP21 VAL = 0. / Cab. 1 outlet temp. [C] HIERARCH ESO INS TEMP22 ID = 'CA1IT ' / Temperature sensor ID. HIERARCH ESO INS TEMP22 NAME = 'Cab.1 inlet temp.' / Temperature sensor name. HIERARCH ESO INS TEMP22 VAL = 0. / Cab. 1 inlet temp. [C] HIERARCH ESO INS TEMP23 ID = 'CA1CT ' / Temperature sensor ID. HIERARCH ESO INS TEMP23 NAME = 'Cab.1 temp.' / Temperature sensor name. HIERARCH ESO INS TEMP23 VAL = 0. / Cab. 1 temp. [C] HIERARCH ESO INS TEMP24 ID = 'CA1AT ' / Temperature sensor ID. HIERARCH ESO INS TEMP24 NAME = 'Cab.1 Amb. temp.' / Temperature sensor name. HIERARCH ESO INS TEMP24 VAL = 0. / Cab. 1 Amb. temp. [C] HIERARCH ESO INS TEMP3 ID = 'TMUB ' / Temperature sensor ID. HIERARCH ESO INS TEMP3 NAME = 'UVB bench temp.' / Temperature sensor name. HIERARCH ESO INS TEMP3 VAL = 14.04 / UVB bench temp. [C] HIERARCH ESO INS TEMP4 ID = 'TMVC ' / Temperature sensor ID. HIERARCH ESO INS TEMP4 NAME = 'VIS Camera temp.' / Temperature sensor name. HIERARCH ESO INS TEMP4 VAL = 13.06 / VIS Camera temp. [C] HIERARCH ESO INS TEMP5 ID = 'TMVP ' / Temperature sensor ID. HIERARCH ESO INS TEMP5 NAME = 'VIS Prism temp.' / Temperature sensor name. HIERARCH ESO INS TEMP5 VAL = 14. / VIS Prism temp. [C] HIERARCH ESO INS TEMP6 ID = 'TMVB ' / Temperature sensor ID. HIERARCH ESO INS TEMP6 NAME = 'VIS bench temp.' / Temperature sensor name. HIERARCH ESO INS TEMP6 VAL = 13.4 / VIS bench temp. [C] HIERARCH ESO INS TEMP7 ID = 'TMUCR ' / Temperature sensor ID. HIERARCH ESO INS TEMP7 NAME = 'UVB cryo. temp.' / Temperature sensor name. HIERARCH ESO INS TEMP7 VAL = 10.32 / UVB cryo. temp. [C] HIERARCH ESO INS TEMP8 ID = 'TMVCR ' / Temperature sensor ID. HIERARCH ESO INS TEMP8 NAME = 'VIS cryo. temp.' / Temperature sensor name. HIERARCH ESO INS TEMP8 VAL = 11.09 / VIS cryo. temp. [C] HIERARCH ESO INS TEMP80 ID = 'OBCT ' / Temperature sensor ID. HIERARCH ESO INS TEMP80 NAME = 'OB Cover Tel.' / Temperature sensor name. HIERARCH ESO INS TEMP80 VAL = 103.96 / OB Cover Telescope [K] HIERARCH ESO INS TEMP81 ID = 'OBCB ' / Temperature sensor ID. HIERARCH ESO INS TEMP81 NAME = 'OB Cover Tank' / Temperature sensor name. HIERARCH ESO INS TEMP81 VAL = 103.87 / OB Cover Tank [K] HIERARCH ESO INS TEMP82 ID = 'OBPR ' / Temperature sensor ID. HIERARCH ESO INS TEMP82 NAME = 'OB Prism1' / Temperature sensor name. HIERARCH ESO INS TEMP82 VAL = 103.29 / OB Prism1 [K] HIERARCH ESO INS TEMP83 ID = 'OBCL ' / Temperature sensor ID. HIERARCH ESO INS TEMP83 NAME = 'OB Corrector Lens' / Temperature sensor name. HIERARCH ESO INS TEMP83 VAL = 104.04 / OB Corrector Lens [K] HIERARCH ESO INS TEMP84 ID = 'CP ' / Temperature sensor ID. HIERARCH ESO INS TEMP84 NAME = 'Cryo. Cold Plate' / Temperature sensor name. HIERARCH ESO INS TEMP84 VAL = 86.332 / Cryostat Cold Plate [K] HIERARCH ESO INS TEMP85 ID = 'RS ' / Temperature sensor ID. HIERARCH ESO INS TEMP85 NAME = 'Cryo. rad. shield' / Temperature sensor name. HIERARCH ESO INS TEMP85 VAL = 107.74 / Cryostat rad. shield [K] HIERARCH ESO INS TEMP86 ID = 'DCB ' / Temperature sensor ID. HIERARCH ESO INS TEMP86 NAME = 'CCD Copper bar' / Temperature sensor name. HIERARCH ESO INS TEMP86 VAL = 77.173 / CCD Copper bar [K] HIERARCH ESO INS TEMP9 ID = 'TMNCR ' / Temperature sensor ID. HIERARCH ESO INS TEMP9 NAME = 'NIR cryo. temp.' / Temperature sensor name. HIERARCH ESO INS TEMP9 VAL = 14.56 / NIR cryo. temp. [C] HIERARCH ESO INS TEMP90 ID = 'NHT ' / Temperature sensor ID. HIERARCH ESO INS TEMP90 NAME = 'NIR Head temp.' / Temperature sensor name. HIERARCH ESO INS TEMP90 VAL = 79. / NIR Head temp. [K] HIERARCH ESO INS TILT1 AFCX = 1.058 / Flexure corr. in X [volt]. HIERARCH ESO INS TILT1 AFCY = 0.482 / Flexure corr. in Y [volt]. HIERARCH ESO INS TILT1 CONVX = 0.208 / VoltToMarcsec conversion in X. HIERARCH ESO INS TILT1 CONVY = 0.269 / VoltToMarcsec conversion in Y. HIERARCH ESO INS TILT1 ENGX = 6.176 / Piezo voltage at end in X [volt]. HIERARCH ESO INS TILT1 ENGY = 4.372 / Piezo voltage at end in Y [volt]. HIERARCH ESO INS TILT1 ID = 'afcs1 ' / AFCS ID. HIERARCH ESO INS TILT1 MODE = 'AUTO ' / AFCS mode. HIERARCH ESO INS TILT1 NAME = 'Flexure comp. UVB' / AFCS description. HIERARCH ESO INS TILT1 OFFSETX = 5. / Offset voltage in X [volt]. HIERARCH ESO INS TILT1 OFFSETY = 5. / Offset voltage in Y [volt]. HIERARCH ESO INS TILT1 POSXEND = 1.227 / Piezo position at end [volt]. HIERARCH ESO INS TILT1 POSXSTART = 1.095 / Piezo position at start in X [volt HIERARCH ESO INS TILT1 POSYEND = -0.578 / Piezo position at end [volt]. HIERARCH ESO INS TILT1 POSYSTART = -0.648 / Piezo position at start in Y [volt HIERARCH ESO INS TILT1 REFRACXEND = 1.266 / Atm. refr. corr. end in X [volt]. HIERARCH ESO INS TILT1 REFRACXST = 0.998 / Atm. refr. corr. start in X [volt] HIERARCH ESO INS TILT1 REFRACYEND = -1.643 / Atm. refr. corr. end in Y [volt]. HIERARCH ESO INS TILT1 REFRACYST = -1.652 / Atm. refr. corr. start in Y [volt] HIERARCH ESO INS TILT2 AFCX = 0.316 / Flexure corr. in X [volt]. HIERARCH ESO INS TILT2 AFCY = 0.17 / Flexure corr. in Y [volt]. HIERARCH ESO INS TILT2 CONVX = 0.291 / VoltToMarcsec conversion in X. HIERARCH ESO INS TILT2 CONVY = 0.384 / VoltToMarcsec conversion in Y. HIERARCH ESO INS TILT2 ENGX = 5.256 / Piezo voltage at end in X [volt]. HIERARCH ESO INS TILT2 ENGY = 6.238 / Piezo voltage at end in Y [volt]. HIERARCH ESO INS TILT2 ID = 'afcs2 ' / AFCS ID. HIERARCH ESO INS TILT2 MODE = 'AUTO ' / AFCS mode. HIERARCH ESO INS TILT2 NAME = 'Flexure comp. VIS' / AFCS description. HIERARCH ESO INS TILT2 OFFSETX = 5. / Offset voltage in X [volt]. HIERARCH ESO INS TILT2 OFFSETY = 5. / Offset voltage in Y [volt]. HIERARCH ESO INS TILT2 POSXEND = 0.165 / Piezo position at end [volt]. HIERARCH ESO INS TILT2 POSXSTART = 0.283 / Piezo position at start in X [volt HIERARCH ESO INS TILT2 POSYEND = 1.099 / Piezo position at end [volt]. HIERARCH ESO INS TILT2 POSYSTART = 1.16 / Piezo position at start in Y [volt HIERARCH ESO INS TILT2 REFRACXEND = -1.301 / Atm. refr. corr. end in X [volt]. HIERARCH ESO INS TILT2 REFRACXST = -1.029 / Atm. refr. corr. start in X [volt] HIERARCH ESO INS TILT2 REFRACYEND = 1.678 / Atm. refr. corr. end in Y [volt]. HIERARCH ESO INS TILT2 REFRACYST = 1.69 / Atm. refr. corr. start in Y [volt] HIERARCH ESO INS TILT3 AFCX = 0.997 / Flexure corr. in X [volt]. HIERARCH ESO INS TILT3 AFCY = 0.261 / Flexure corr. in Y [volt]. HIERARCH ESO INS TILT3 CONVX = 0.58 / VoltToMarcsec conversion in X. HIERARCH ESO INS TILT3 CONVY = 0.56 / VoltToMarcsec conversion in Y. HIERARCH ESO INS TILT3 ENGX = 7.308 / Piezo voltage at end in X [volt]. HIERARCH ESO INS TILT3 ENGY = 5.333 / Piezo voltage at end in Y [volt]. HIERARCH ESO INS TILT3 ID = 'afcs3 ' / AFCS ID. HIERARCH ESO INS TILT3 MODE = 'AUTO ' / AFCS mode. HIERARCH ESO INS TILT3 NAME = 'Flexure comp. NIR' / AFCS description. HIERARCH ESO INS TILT3 OFFSETX = 5. / Offset voltage in X [volt]. HIERARCH ESO INS TILT3 OFFSETY = 5. / Offset voltage in Y [volt]. HIERARCH ESO INS TILT3 POSXEND = 2.162 / Piezo position at end [volt]. HIERARCH ESO INS TILT3 POSXSTART = 2.238 / Piezo position at start in X [volt HIERARCH ESO INS TILT3 POSYEND = 0.411 / Piezo position at end [volt]. HIERARCH ESO INS TILT3 POSYSTART = 0.294 / Piezo position at start in Y [volt HIERARCH ESO INS TILT3 REFRACXEND = -1.418 / Atm. refr. corr. end in X [volt]. HIERARCH ESO INS TILT3 REFRACXST = -1.147 / Atm. refr. corr. start in X [volt] HIERARCH ESO INS TILT3 REFRACYEND = 1.795 / Atm. refr. corr. end in Y [volt]. HIERARCH ESO INS TILT3 REFRACYST = 1.806 / Atm. refr. corr. start in Y [volt] HIERARCH ESO DET BITS = 16 / Bits per pixel readout HIERARCH ESO DET CHIP1 DATE = '2005-04-22' / Date of installation [YYYY-MM-DD] HIERARCH ESO DET CHIP1 ID = 'Catherine' / Detector chip identification HIERARCH ESO DET CHIP1 INDEX = 1 / Chip index HIERARCH ESO DET CHIP1 NAME = 'MIT/LL CCID-20' / Detector chip name HIERARCH ESO DET CHIP1 NX = 2048 / # of pixels along X HIERARCH ESO DET CHIP1 NY = 4102 / # of pixels along Y HIERARCH ESO DET CHIP1 PSZX = 15. / Size of pixel in X HIERARCH ESO DET CHIP1 PSZY = 15. / Size of pixel in Y HIERARCH ESO DET CHIP1 X = 1 / X location in array HIERARCH ESO DET CHIP1 XGAP = 0. / Gap between chips along x HIERARCH ESO DET CHIP1 Y = 1 / Y location in array HIERARCH ESO DET CHIP1 YGAP = 0. / Gap between chips along y HIERARCH ESO DET CHIPS = 1 / # of chips in detector array HIERARCH ESO DET DATE = '2005-04-22' / Installation date HIERARCH ESO DET DEC = 0. / Apparent 00:00:00.0 DEC at start HIERARCH ESO DET DID = 'ESO-VLT-DIC.CCDDCS,ESO-VLT-DIC.FCDDCS' / Diction HIERARCH ESO DET EXP NO = 321 / Unique exposure ID number HIERARCH ESO DET EXP RDTTIME = 4.044 / image readout time HIERARCH ESO DET EXP TYPE = 'Normal ' / Exposure type HIERARCH ESO DET EXP XFERTIM = 90.351 / image transfer time HIERARCH ESO DET FRAM ID = 1 / Image sequencial number HIERARCH ESO DET FRAM TYPE = 'Normal ' / Type of frame HIERARCH ESO DET ID = 'CCD FIERA - Rev: 238334' / Detector system Id HIERARCH ESO DET NAME = 'shdetv - shdetv' / Name of detector system HIERARCH ESO DET OUT1 CHIP = 1 / Chip to which the output belongs HIERARCH ESO DET OUT1 CONAD = 0.64 / Conversion from ADUs to electrons HIERARCH ESO DET OUT1 GAIN = 1.56 / Conversion from electrons to ADU HIERARCH ESO DET OUT1 ID = 'A ' / Output ID as from manufacturer HIERARCH ESO DET OUT1 INDEX = 1 / Output index HIERARCH ESO DET OUT1 NAME = 'A ' / Description of output HIERARCH ESO DET OUT1 NX = 2048 / valid pixels along X HIERARCH ESO DET OUT1 NY = 4000 / valid pixels along Y HIERARCH ESO DET OUT1 OVSCX = 48 / Overscan region in X HIERARCH ESO DET OUT1 OVSCY = 0 / Overscan region in Y HIERARCH ESO DET OUT1 PRSCX = 10 / Prescan region in X HIERARCH ESO DET OUT1 PRSCY = 0 / Prescan region in Y HIERARCH ESO DET OUT1 RON = 3.4 / [e-] Readout noise per output (ele HIERARCH ESO DET OUT1 X = 1 / X location of output HIERARCH ESO DET OUT1 Y = 1 / Y location of output HIERARCH ESO DET OUTPUTS = 1 / # of outputs HIERARCH ESO DET OUTREF = 0 / reference output HIERARCH ESO DET RA = 0. / Apparent 00:00:00.0 RA at start HIERARCH ESO DET READ CLOCK = '100k/1pt/hg' / Readout clock pattern used HIERARCH ESO DET READ MODE = 'normal ' / Readout method HIERARCH ESO DET READ NFRAM = 1 / Number of readouts buffered in sin HIERARCH ESO DET READ SPEED = '1pt/100k/hg' / Readout speed HIERARCH ESO DET SHUT ID = 'VA 25mm BistableCCD Sensor1' / Shutter unique id HIERARCH ESO DET SHUT TMCLOS = 0. / Time taken to close shutter HIERARCH ESO DET SHUT TMOPEN = 0. / Time taken to open shutter HIERARCH ESO DET SHUT TYPE = 'Iris on Slit' / type of shutter HIERARCH ESO DET SOFW MODE = 'Normal ' / CCD sw operational mode HIERARCH ESO DET TELE INT = 600. / Interval between two successive te HIERARCH ESO DET TELE NO = 4 / # of sources active HIERARCH ESO DET TLM1 END = 135. / Telemetry value at read completion HIERARCH ESO DET TLM1 ID = 'CCD Sensor1' / ID of telemetry sensor HIERARCH ESO DET TLM1 NAME = 'CCD T1 ' / Description of telemetry param. HIERARCH ESO DET TLM1 START = 135. / Telemetry value at read start HIERARCH ESO DET TLM2 END = 143.3 / Telemetry value at read completion HIERARCH ESO DET TLM2 ID = 'CCD Sensor2' / ID of telemetry sensor HIERARCH ESO DET TLM2 NAME = 'CCD T2 ' / Description of telemetry param. HIERARCH ESO DET TLM2 START = 143.3 / Telemetry value at read start HIERARCH ESO DET TLM3 END = 0. / Telemetry value at read completion HIERARCH ESO DET TLM3 ID = 'Box Temp' / ID of telemetry sensor HIERARCH ESO DET TLM3 NAME = 'EBOX T ' / Description of telemetry param. HIERARCH ESO DET TLM3 START = 0. / Telemetry value at read start HIERARCH ESO DET TLM4 END = 0. / Telemetry value at read completion HIERARCH ESO DET TLM4 ID = 'Vacuum ' / ID of telemetry sensor HIERARCH ESO DET TLM4 NAME = 'Vacuum ' / Description of telemetry param. HIERARCH ESO DET TLM4 START = 0. / Telemetry value at read start HIERARCH ESO DET WIN1 BINX = 1 / Binning factor along X HIERARCH ESO DET WIN1 BINY = 1 / Binning factor along Y HIERARCH ESO DET WIN1 DIT1 = 220.000006 / actual subintegration time HIERARCH ESO DET WIN1 DKTM = 220.3678 / Dark current time HIERARCH ESO DET WIN1 NDIT = 1 / # of subintegrations HIERARCH ESO DET WIN1 NX = 2106 / # of pixels along X HIERARCH ESO DET WIN1 NY = 4000 / # of pixels along Y HIERARCH ESO DET WIN1 ST = T / If T, window enabled HIERARCH ESO DET WIN1 STRX = 1 / Lower left pixel in X HIERARCH ESO DET WIN1 STRY = 1 / Lower left pixel in Y HIERARCH ESO DET WIN1 UIT1 = 220. / user defined subintegration time HIERARCH ESO DET WINDOWS = 1 / # of windows readout HIERARCH ESO PRO CATG = 'SCIENCE_TELLURIC_CORR' / Category of pipeline product fHIERARCH ESO PRO B RELOFF RA = 3.502468 HIERARCH ESO PRO B RELOFF DEC = -3.301557 HIERARCH ESO PRO B CUMOFF RA = 1.758225 HIERARCH ESO PRO B CUMOFF DEC = -1.657368 HIERARCH ESO PRO RECT BIN LAMBDA = 0.02 HIERARCH ESO PRO RECT BIN SPACE = 0.16 HIERARCH ESO PRO RECT LAMBDA MIN = 533.66 HIERARCH ESO PRO RECT LAMBDA MAX = 1020. HIERARCH ESO PRO RECT SPACE MIN = -10.1000003814697 HIERARCH ESO PRO RECT SPACE MAX = 5.41999959945679 HIERARCH ESO PRO EXTRACT SLIT MIN = 0. HIERARCH ESO PRO EXTRACT SLIT MAX = 0. HIERARCH ESO PRO ANCESTOR = 'XSHOO.2021-12-10T07:05:43.478.fits' HIERARCH ESO PRO DID = 'PRO-1.16' / Data dictionary for PRO HIERARCH ESO PRO TYPE = 'REDUCED ' / Product type HIERARCH ESO PRO TECH = 'ECHELLE,SLIT,NODDING' / Observation technique HIERARCH ESO PRO SCIENCE = T / Scientific product if T HIERARCH ESO PRO REC1 ID = 'xsh_scired_slit_nod' / Pipeline recipe (unique) idenHIERARCH ESO PRO REC1 DRS ID = 'cpl-7.1.4' / Data Reduction System identifier HIERARCH ESO PRO REC1 PIPE ID = 'xshoo/3.5.3' / Pipeline (unique) identifier HIERARCH ESO PRO REC1 RAW1 NAME = 'XSHOO.2021-12-10T07:05:43.478.fits' / File naHIERARCH ESO PRO REC1 RAW1 CATG = 'OBJECT_SLIT_NOD_VIS' / Category of raw frame HIERARCH ESO PRO REC1 RAW2 NAME = 'XSHOO.2021-12-10T07:16:25.221.fits' / File naHIERARCH ESO PRO REC1 RAW2 CATG = 'OBJECT_SLIT_NOD_VIS' / Category of raw frame HIERARCH ESO PRO DATANCOM = 2 / Number of combined frames HIERARCH ESO PRO REC1 CAL1 NAME = 'xsh_paranal_extinct_model_vis.fits' / File naHIERARCH ESO PRO REC1 CAL1 CATG = 'ATMOS_EXT_VIS' / Category of calibration framHIERARCH ESO PRO REC1 CAL1 DATAMD5 = '127f77fd815204958285517e8b21a214' / MD5 siHIERARCH ESO PRO REC1 CAL2 NAME = 'BP_MAP_RP_VIS_1x1.fits' / File name of calibrHIERARCH ESO PRO REC1 CAL2 CATG = 'BP_MAP_RP_VIS' / Category of calibration framHIERARCH ESO PRO REC1 CAL2 DATAMD5 = '45b68d9dd3bfb2211887f5112de96d92' / MD5 siHIERARCH ESO PRO REC1 CAL3 NAME = 'DISP_TAB_AFC_VIS.fits' / File name of calibraHIERARCH ESO PRO REC1 CAL3 CATG = 'DISP_TAB_AFC_VIS' / Category of calibration fHIERARCH ESO PRO REC1 CAL3 DATAMD5 = 'ce31a89355ed060ceac0762f476cc466' / MD5 siHIERARCH ESO PRO REC1 CAL4 NAME = 'DISP_TAB_VIS.fits' / File name of calibrationHIERARCH ESO PRO REC1 CAL4 CATG = 'DISP_TAB_VIS' / Category of calibration frameHIERARCH ESO PRO REC1 CAL4 DATAMD5 = '6b7b5ec102b28b255ee0bbec47ed8e87' / MD5 siHIERARCH ESO PRO REC1 CAL5 NAME = 'MASTER_BIAS_VIS.fits' / File name of calibratHIERARCH ESO PRO REC1 CAL5 CATG = 'MASTER_BIAS_VIS' / Category of calibration frHIERARCH ESO PRO REC1 CAL5 DATAMD5 = 'f67302bc059e111a23261481dab3e023' / MD5 siHIERARCH ESO PRO REC1 CAL6 NAME = 'MASTER_FLAT_SLIT_VIS.fits' / File name of calHIERARCH ESO PRO REC1 CAL6 CATG = 'MASTER_FLAT_SLIT_VIS' / Category of calibratiHIERARCH ESO PRO REC1 CAL6 DATAMD5 = 'c1878e55eeb5602da9953343be5f7b19' / MD5 siHIERARCH ESO PRO REC1 CAL7 NAME = 'ORDER_TAB_AFC_SLIT_VIS.fits' / File name of cHIERARCH ESO PRO REC1 CAL7 CATG = 'ORDER_TAB_AFC_SLIT_VIS' / Category of calibraHIERARCH ESO PRO REC1 CAL7 DATAMD5 = '300ab8e8540a617d8b46fd118c9b174e' / MD5 siHIERARCH ESO PRO REC1 CAL8 NAME = 'ORDER_TAB_EDGES_SLIT_VIS.fits' / File name ofHIERARCH ESO PRO REC1 CAL8 CATG = 'ORDER_TAB_EDGES_SLIT_VIS' / Category of calibHIERARCH ESO PRO REC1 CAL8 DATAMD5 = '17dc70d74423301f8b9a40256e084570' / MD5 siHIERARCH ESO PRO REC1 CAL9 NAME = 'RESPONSE_MERGE1D_SLIT_VIS.fits' / File name oHIERARCH ESO PRO REC1 CAL9 CATG = 'RESPONSE_MERGE1D_SLIT_VIS' / Category of caliHIERARCH ESO PRO REC1 CAL9 DATAMD5 = '66ef9a53a6e917d32f4cd8023a2238d0' / MD5 siHIERARCH ESO PRO REC1 CAL10 NAME = 'SPECTRAL_FORMAT_TAB_VIS.fits' / File name ofHIERARCH ESO PRO REC1 CAL10 CATG = 'SPECTRAL_FORMAT_TAB_VIS' / Category of calibHIERARCH ESO PRO REC1 CAL10 DATAMD5 = '9b447dab81b23bf0fbccda78f6fdc79d' / MD5 sHIERARCH ESO PRO REC1 CAL11 NAME = 'XSH_MOD_CFG_OPT_2D_VIS.fits' / File name of HIERARCH ESO PRO REC1 CAL11 CATG = 'XSH_MOD_CFG_OPT_2D_VIS' / Category of calibrHIERARCH ESO PRO REC1 CAL11 DATAMD5 = '2698339025f43ae909884be1eb9c01ef' / MD5 sHIERARCH ESO PRO REC1 CAL12 NAME = 'XSH_MOD_CFG_OPT_AFC_VIS.fits' / File name ofHIERARCH ESO PRO REC1 CAL12 CATG = 'XSH_MOD_CFG_OPT_AFC_VIS' / Category of calibHIERARCH ESO PRO REC1 CAL12 DATAMD5 = 'b09b2db9069a0bad01e0c4de255cc96f' / MD5 sHIERARCH ESO PRO REC1 PARAM1 NAME = 'keep-temp' / If 'no', temporary files are dHIERARCH ESO PRO REC1 PARAM1 VALUE = 'no ' / Default: 'no' HIERARCH ESO PRO REC1 PARAM2 NAME = 'debug-level' / Additional xshooter debug leHIERARCH ESO PRO REC1 PARAM2 VALUE = 'none ' / Default: 'none' HIERARCH ESO PRO REC1 PARAM3 NAME = 'time-stamp' / Add timestamp to product fileHIERARCH ESO PRO REC1 PARAM3 VALUE = 'false ' / Default: false HIERARCH ESO PRO REC1 PARAM4 NAME = 'decode-bp' / Integer representation of the HIERARCH ESO PRO REC1 PARAM4 VALUE = '1741684735' / Default: 1741684735 HIERARCH ESO PRO REC1 PARAM5 NAME = 'pre-overscan-corr' / pre-overscan correctioHIERARCH ESO PRO REC1 PARAM5 VALUE = '1 ' / Default: 1 HIERARCH ESO PRO REC1 PARAM6 NAME = 'stack-method' / Method used to build masterHIERARCH ESO PRO REC1 PARAM6 VALUE = 'median ' / Default: 'median' HIERARCH ESO PRO REC1 PARAM7 NAME = 'klow ' / Kappa used to clip low level vaHIERARCH ESO PRO REC1 PARAM7 VALUE = '5 ' / Default: 5 HIERARCH ESO PRO REC1 PARAM8 NAME = 'khigh ' / Kappa used to clip high level vHIERARCH ESO PRO REC1 PARAM8 VALUE = '5 ' / Default: 5 HIERARCH ESO PRO REC1 PARAM9 NAME = 'removecrhsingle-sigmalim' / Poisson fluctuaHIERARCH ESO PRO REC1 PARAM9 VALUE = '20 ' / Default: 20 HIERARCH ESO PRO REC1 PARAM10 NAME = 'removecrhsingle-flim' / Minimum contrast bHIERARCH ESO PRO REC1 PARAM10 VALUE = '2 ' / Default: 2 HIERARCH ESO PRO REC1 PARAM11 NAME = 'removecrhsingle-niter' / Max number of iteHIERARCH ESO PRO REC1 PARAM11 VALUE = '4 ' / Default: 4 HIERARCH ESO PRO REC1 PARAM12 NAME = 'rectify-kernel' / Name of the InterpolatioHIERARCH ESO PRO REC1 PARAM12 VALUE = 'tanh ' / Default: 'tanh' HIERARCH ESO PRO REC1 PARAM13 NAME = 'rectify-radius' / Rectify Interpolation raHIERARCH ESO PRO REC1 PARAM13 VALUE = '2 ' / Default: 2 HIERARCH ESO PRO REC1 PARAM14 NAME = 'rectify-bin-lambda' / Wavelength step in tHIERARCH ESO PRO REC1 PARAM14 VALUE = '0.02 ' / Default: -1 HIERARCH ESO PRO REC1 PARAM15 NAME = 'rectify-bin-slit' / Spatial step along theHIERARCH ESO PRO REC1 PARAM15 VALUE = '0.16 ' / Default: -1 HIERARCH ESO PRO REC1 PARAM16 NAME = 'rectify-fast' / Fast if TRUE (Rect[B-A] = HIERARCH ESO PRO REC1 PARAM16 VALUE = 'true ' / Default: true HIERARCH ESO PRO REC1 PARAM17 NAME = 'localize-method' / Localization method (MAHIERARCH ESO PRO REC1 PARAM17 VALUE = 'MANUAL ' / Default: 'MANUAL' HIERARCH ESO PRO REC1 PARAM18 NAME = 'localize-chunk-nb' / Number of chunks in tHIERARCH ESO PRO REC1 PARAM18 VALUE = '10 ' / Default: 10 HIERARCH ESO PRO REC1 PARAM19 NAME = 'localize-thresh' / Threshold relative to tHIERARCH ESO PRO REC1 PARAM19 VALUE = '0.1 ' / Default: 0.1 HIERARCH ESO PRO REC1 PARAM20 NAME = 'localize-deg-lambda' / Degree in lambda inHIERARCH ESO PRO REC1 PARAM20 VALUE = '0 ' / Default: 0 HIERARCH ESO PRO REC1 PARAM21 NAME = 'localize-slit-position' / Object position HIERARCH ESO PRO REC1 PARAM21 VALUE = '0 ' / Default: 0 HIERARCH ESO PRO REC1 PARAM22 NAME = 'localize-slit-hheight' / Object half heighHIERARCH ESO PRO REC1 PARAM22 VALUE = '2 ' / Default: 2 HIERARCH ESO PRO REC1 PARAM23 NAME = 'localize-kappa' / Kappa value for sigma clHIERARCH ESO PRO REC1 PARAM23 VALUE = '3 ' / Default: 3 HIERARCH ESO PRO REC1 PARAM24 NAME = 'localize-niter' / Number of iterations forHIERARCH ESO PRO REC1 PARAM24 VALUE = '3 ' / Default: 3 HIERARCH ESO PRO REC1 PARAM25 NAME = 'localize-use-skymask' / TRUE if we want toHIERARCH ESO PRO REC1 PARAM25 VALUE = 'false ' / Default: false HIERARCH ESO PRO REC1 PARAM26 NAME = 'localize-nod-throw' / Step (arcsec) betweeHIERARCH ESO PRO REC1 PARAM26 VALUE = '0 ' / Default: 0 HIERARCH ESO PRO REC1 PARAM27 NAME = 'extract-method' / Method used for extractiHIERARCH ESO PRO REC1 PARAM27 VALUE = 'NOD ' / Default: 'NOD' HIERARCH ESO PRO REC1 PARAM28 NAME = 'stdextract-interp-hsize' / Half size of maHIERARCH ESO PRO REC1 PARAM28 VALUE = '30 ' / Default: 30 HIERARCH ESO PRO REC1 PARAM29 NAME = 'combinenod-throwlist' / Name of ascii fileHIERARCH ESO PRO REC1 PARAM29 VALUE = 'throwlist.asc' / Default: 'throwlist.asc'HIERARCH ESO PRO REC1 PARAM30 NAME = 'combinenod-method' / Combination method foHIERARCH ESO PRO REC1 PARAM30 VALUE = 'MEAN ' / Default: 'MEAN' HIERARCH ESO PRO REC1 PARAM31 NAME = 'max-slit' / Lower Slit Limit (localize andHIERARCH ESO PRO REC1 PARAM31 VALUE = '5.7 ' / Default: 5.7 HIERARCH ESO PRO REC1 PARAM32 NAME = 'min-slit' / Upper Slit Limit (localize andHIERARCH ESO PRO REC1 PARAM32 VALUE = '-5.3 ' / Default: -5.3 HIERARCH ESO PRO REC1 PARAM33 NAME = 'correct-sky-by-median' / TRUE if the resamHIERARCH ESO PRO REC1 PARAM33 VALUE = 'true ' / Default: true HIERARCH ESO PRO REC1 PARAM34 NAME = 'cut-uvb-spectrum' / TRUE if recipe cuts thHIERARCH ESO PRO REC1 PARAM34 VALUE = 'true ' / Default: true HIERARCH ESO PRO REC1 PARAM35 NAME = 'generate-SDP-format' / TRUE if additional HIERARCH ESO PRO REC1 PARAM35 VALUE = 'true ' / Default: false HIERARCH ESO PRO REC1 PARAM36 NAME = 'dummy-association-keys' / Sets the number HIERARCH ESO PRO REC1 PARAM36 VALUE = '0 ' / Default: 0 HIERARCH ESO PRO REC1 PARAM37 NAME = 'scale-combine-nod-method' / frame scaling HIERARCH ESO PRO REC1 PARAM37 VALUE = '1 ' / Default: 1 HIERARCH ESO PRO REC2 ID = 'molecfit_correct' / Pipeline recipe (unique) identifHIERARCH ESO PRO REC2 DRS ID = 'cpl-7.1.4' / Data Reduction System identifier HIERARCH ESO PRO REC2 PIPE ID = 'molecfit/4.1.1' / Pipeline (unique) identifier HIERARCH ESO PRO REC2 RAW1 NAME = 'TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_VIS.fits'HIERARCH ESO PRO REC2 RAW1 CATG = 'SCIENCE ' / Category of raw frame HIERARCH ESO PRO REC2 PARAM1 NAME = 'USE_ONLY_INPUT_PRIMARY_DATA' / Value=TRUE iHIERARCH ESO PRO REC2 PARAM1 VALUE = 'true ' / Default: false HIERARCH ESO PRO REC2 PARAM2 NAME = 'USE_DATA_EXTENSION_AS_DFLUX' / Only valid iHIERARCH ESO PRO REC2 PARAM2 VALUE = '1 ' / Default: 0 HIERARCH ESO PRO REC2 PARAM3 NAME = 'USE_DATA_EXTENSION_AS_MASK' / Only valid ifHIERARCH ESO PRO REC2 PARAM3 VALUE = '2 ' / Default: 0 HIERARCH ESO PRO REC2 PARAM4 NAME = 'SUPPRESS_EXTENSION' / Suppress arbitrary fiHIERARCH ESO PRO REC2 PARAM4 VALUE = 'false ' / Default: false HIERARCH ESO PRO REC2 PARAM5 NAME = 'MAPPING_CORRECT' / Mapping 'SCIENCE' - 'TELHIERARCH ESO PRO REC2 PARAM5 VALUE = '1 ' / Default: 'NULL' HIERARCH ESO PRO REC2 PARAM6 NAME = 'CHIP_EXTENSIONS' / Flag that determines if HIERARCH ESO PRO REC2 PARAM6 VALUE = 'false ' / Default: false HIERARCH ESO ADA ABSROT END = 173.13576 / Abs rot angle at exp end HIERARCH ESO ADA ABSROT PPOS = 'POS ' / sign of probe position HIERARCH ESO ADA ABSROT START = 167.54384 / Abs rot angle at exp start HIERARCH ESO ADA GUID DEC = -55.27821 / -55:16:41.5 Guide star DEC J2000 HIERARCH ESO ADA GUID RA = 142.805983 / 09:31:13.4 Guide star RA J2000 HIERARCH ESO ADA GUID STATUS = 'ON ' / Status of autoguider HIERARCH ESO ADA POSANG = 46.69136 / Position angle at start HIERARCH ESO OCS DET2 IMGNAME = 'XSHOOTER_SLT_OBJ_VIS_' / Data File Name. HIERARCH ESO SEQ ARM = 'VIS ' / Instrument Arm HIERARCH ESO SEQ CUMOFF DEC = 1.644188 / Cumulative DEC offset HIERARCH ESO SEQ CUMOFF RA = -1.744243 / Cumulative RA offset HIERARCH ESO SEQ CUMOFF X = 0. / Cumulative offset X (perp. to slit HIERARCH ESO SEQ CUMOFF Y = 2.397027 / Cumulative offset Y (along the sli HIERARCH ESO SEQ JITTER WIDTH = 0.5 / Width of the Jitter box (arcsec) HIERARCH ESO SEQ NOD THROW = 5. / Nodding throw length in arcsec HIERARCH ESO SEQ RELOFF DEC = 1.644188 / List of DEC offsets HIERARCH ESO SEQ RELOFF RA = -1.744243 / List of RA offsets HIERARCH ESO QC NPIXSAT = 0. / Number of saturated pixels HIERARCH ESO QC FPIXSAT = 0. / Fraction of saturated pixels HIERARCH ESO QC NPIXRANG = 19. / Number of pixels in range 4800-5200 ADU HIERARCH ESO QC FPIXRANG = 2.3193359375E-06 / Frac of pix in range 4800-5200 ADUHIERARCH ESO QC CRRATE = 0.1282355 / Number of detected CRH per cm2 and s HIERARCH ESO QC NCRH = 520 / Number of detected cosmic ray hits HIERARCH ESO QC NCRH AVG = 520. / Average number of cosmic ray hits per frame HIERARCH ESO QC NCRH TOT = 1040 / Total number of cosmic ray hits on frames HIERARCH ESO QC VRAD BARYCOR = 13.5271448611554 / Barycentric radial velocity coHIERARCH ESO QC VRAD HELICOR = 13.524373592839 / Heliocentric radial velocity coCOMMENT FITS (Flexible Image Transport System) format is defined in 'AstronomyCOMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H CHECKSUM= 'NfADQdABNdABNdAB' / HDU checksum updated 2021-12-13T23:42:20 DATASUM = '2084840683' / data unit checksum updated 2021-12-13T23:42:20 END ="^첰9-/"Ҳ@=٫0Ͻ CM>Z{=%naS[/=(D P<I^= \u,J=2Ӽj1JKLT=#%;=)ֺg'=;|q<ݕJ0$wvsͩ&1~5)\3x=kݳ.s:9eA= +H^0qnTjg;=m]e<= R0=z3<@ <~B<+7#6@lH<6{9H=RP[=w{vl=ow=Xʟ<a*5-7c<0Qz͢=P%jӺ\=!<ԋ@IeI1=,O<ܾ$sE=yA<" 顼TS<[\<~^Z<6wM<6v< 4<զp$<逼:=_<*Y#<||]|<$g <_vA`<^c?w<eay<_`38;~J=Y<|:"<@u!<,Ԯ -<\s+:<){<yC'6<-r<̤S}t<Ì!<8}<ҕ><;p,n<ϣ/dð<[A<}j<2<̗?V< #FaEu<~4~<`<):JI<@I'7<{^F<[ p<~%'<eQ"<}<<$|<7H<_U< +6<]<<b8<<\!<;a<*<;Ng<W;D|<=MqـZ<~ <c<&Q e#S'S 7<b <6 |1!<{/V<[KgF<rD@<79h<|D=jD<Xs<D< &Z< £mp̂#:M<|2Y֌<- +<~?<im<BJ<ê< q0b<B<*<`wX< LݚF/?Lb<ߠv?<,M<^\n<nt<=m<e <$nSK v<.[<ýC<BK<? u%<&?)<'x`<]_ J-z<9 +syOc<70<%Z5va<9R!<< +<^Pj<1<#y<0<<䱤<.<%f<7;&< +f] +b|M<2e<sz< iLiPJ<_W <r <8靹c Py< ><[,t<h˖<ޠn3pھ <=6<+f <,0*<61sӷ5<_}<qe"<^ T<C<`<5<㪯<,ừ<'x<$&\<ةse<[}<-P<Ҥę}<3T<|<1(<ȥsc<:<%{<4M,< @\<׆g)<%A<^<R"5c<92<̵?`<\A<2<ѦgD<󷏶hpﶒ*y#<\*p<-m,N9m<w <s< ~h<|0<K1<]<6<-T3w<\h'L<+0J<&R<7F" 4L<ݴQ`<ܒK<"<ˌ</<Pi|+<9lp4oe90<jt<>+=2<;e?I4<8z*t=@< +Τ"<]A=<mg<=~Jv'<C!O|<^<?!<ܠl<^eeo/<鶬< e6< +E9<|b)<z-~H<㒣r$<<s4<f< Q3G<#t<0&<("ή\<)us/vz?.A<<<g<܆ӞPr< I41m<%d +Y>B=а` +<<3<@ˁ<Чg'\<>L|SKRj#<ܖV6<S\%H^Y<[p|<=Pqy<*PK!<(So<'<w`< Ox< #dnZ< c:<q<_<~L%_Ws<g<0J}<6g<(X=h<Zvo<~,<\ Tk +<]<+<~$<&\ r/a<Ȍ' U{UAz&<+<9\j x<<6s<Ն < Bxz<ޜXH.!<%=v}[ +<ў(<<5M<}Y]<9}-k<-bqt<5? 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No?\.V?d,Ԛ@L@7{?(]ȶ?`"b@b?}Ѭf?a!K@F6:? ڵ+?_ @4?k{?Z3@ ?b֜42?`a뫰@WJa?^oz?]&`Og@ްd#?1?^9TNJ@ fZ?yNGY?c޴@aEބ?wnf?`fȅw@[X?r|?aGi@ ?DK?aX Z*@d(˩?R?Zܠ&0@"Ɣ?e^,?\RRr@. l?[c5?`QàO@9_D3?sNU?bsZ?ǂ@DM?5?[ϛ +CR@P}?*?`:@[Tc?hty?Xjjd @fzx??S,8b@q?#A8b?\G>rQP@}B?C??b˶@7?H?]\[k=@ܴB?9?WX آ@(2?Fո?U4S@tW?'?Nf@{?Nk ?]cI@*U?׎Ҍ!?`J@Q?-!+?el!@י*)?`?cT'~@|wa??cun)t@'3x?qǬ?c2<3y@l.l?ۺ?aC=@i=?'S?c@?b ?`^@9Am?c?ehnf?@&{آ?wi?eL@1?93A?e/ܐXY@@H>>?٪l2?`ϪM@S~ST?mo<v?_`;l}7@^H?7~?cܠ@iX?z?dM[A@u7H?>?` +3@t2?B?`l@ +?堧D?_} y@gv?['!Si9?`أ@$S?\?`GV@]vp:?Q8?_W$34@0Q??Z]?^D4N@^A3?HY?b8: @#?^?aէ'@:Y\? +A?`;@oQb?N6?b6<&O@?N!?d2؉@Xt?)k?aӈcc'@ +-?{4?c̸ҿ@<8{?H?d+TMq@mz?N?a _O@(?KI?do:B@3͡!9?Š¦?auEY@>?ˠ*W?a*>@J*?A)K?a0ܳ@UW?.Pi?c*@`z~?;հ4?a"q@k8?wX?eC2@v-i?U?\Ӌ@Xp?qc8?Z IW@.٭?\V?Y + f \ No newline at end of file diff --git a/examples/data/pepsir.20230603.009.dxt.nor b/examples/data/pepsir.20230603.009.dxt.nor new file mode 100644 index 0000000..5991100 --- /dev/null +++ b/examples/data/pepsir.20230603.009.dxt.nor @@ -0,0 +1,1292 @@ +SIMPLE = T / Standard FITS format BITPIX = 8 / Bits per pixel NAXIS = 0 / Number of axes EXTEND = T / Extension is present MOSAIC = T / Image is CCD mosaic DETSIZE = '[1:10560,1:10560]' / Detector size DETECTOR= 'STA1600LN' / STA Archon controller X12-F 1.0.1028 CBIN = 1 / Binning factor in CCD columns RBIN = 5 / Binning factor in CCD rows GAIN1 = 1.550280 / CCD amplifier gain factor in e/ADU GAIN2 = 1.553919 / CCD amplifier gain factor in e/ADU GAIN3 = 1.559687 / CCD amplifier gain factor in e/ADU GAIN4 = 1.539479 / CCD amplifier gain factor in e/ADU GAIN5 = 1.562029 / CCD amplifier gain factor in e/ADU GAIN6 = 1.551208 / CCD amplifier gain factor in e/ADU GAIN7 = 1.558944 / CCD amplifier gain factor in e/ADU GAIN8 = 1.552657 / CCD amplifier gain factor in e/ADU GAIN9 = 1.522927 / CCD amplifier gain factor in e/ADU GAIN10 = 1.549798 / CCD amplifier gain factor in e/ADU GAIN11 = 1.550713 / CCD amplifier gain factor in e/ADU GAIN12 = 1.558488 / CCD amplifier gain factor in e/ADU GAIN13 = 1.550717 / CCD amplifier gain factor in e/ADU GAIN14 = 1.551005 / CCD amplifier gain factor in e/ADU GAIN15 = 1.561832 / CCD amplifier gain factor in e/ADU GAIN16 = 1.588823 / CCD amplifier gain factor in e/ADU GAINS1 = 0.132443 / CCD amplifier gain slope times 65535 GAINS2 = 0.148397 / CCD amplifier gain slope times 65535 GAINS3 = 0.126385 / CCD amplifier gain slope times 65535 GAINS4 = 0.140005 / CCD amplifier gain slope times 65535 GAINS5 = 0.129050 / CCD amplifier gain slope times 65535 GAINS6 = 0.128416 / CCD amplifier gain slope times 65535 GAINS7 = 0.131469 / CCD amplifier gain slope times 65535 GAINS8 = 0.129429 / CCD amplifier gain slope times 65535 GAINS9 = 0.120627 / CCD amplifier gain slope times 65535 GAINS10 = 0.123148 / CCD amplifier gain slope times 65535 GAINS11 = 0.127687 / CCD amplifier gain slope times 65535 GAINS12 = 0.120984 / CCD amplifier gain slope times 65535 GAINS13 = 0.125144 / CCD amplifier gain slope times 65535 GAINS14 = 0.131374 / CCD amplifier gain slope times 65535 GAINS15 = 0.130235 / CCD amplifier gain slope times 65535 GAINS16 = 0.127412 / CCD amplifier gain slope times 65535 READOUT = 17.650 / CCD camera readout time, sec TRANSFER= 1.003 / CCD image transfer time, sec ARM = 'Red' / CCD camera name CCDTEMP1= 152.9997 / CCD temperature in K at read out CCDTEMP2= 152.9353 / CCD temperature in K at read out CCDTEMP3= 153.8814 / CCD temperature in K at read out DEWATEMP= 110.0790 / Dewar temperature in K DEWARPRE= 1154.6 / Dewar pressure in nanobar DEWARATE= 7.6 / CryoCon heater rate in percent DATE-OBS= '2023-06-03' / UTC date of exposure start TIME-OBS= '09:51:41.441' / UTC time of exposure start EXPTIME = 3600.000 / Effective exposure time INSTRUME= 'PEPSI' / Data acquisition instrument ORIGIN = 'SDS' / Spectroscopic Data Systems CHAPRESS= 700.9991 / Chamber pressure in mbar CHATEMPE= 19.7935 / Chamber table mean temperature in C CHAHUMID= 5.4434 / Chamber relative humidity in percent CHARAVE = 2526.319 / Chamber air-vacuum radial velocity m/s GRATING = 20.0705 / Echelle grating temperature, C IMAGETYP= 'object' / PFU@LBT exposure PID = 'Cusano' / Project ID SEEINGSX= 0.623 / SX guider image seeing in arcsec SEEINGDX= 0.599 / DX guider image seeing in arcsec LBTP = 690.800 / Air Pressure, hPa LBTT = 5.70 / Air Temperature, C LBTH = 45.0 / Air Humidity, percent LBTW = 2.7 / Air WindSpeed, m/s LBTRA = '21:38:10.8' / Telescope Right Ascension LBTDE = '+26:27:59.7' / Telescope Declination LBTAZ = '-075:59:23.3' / Telescope Azimuth LBTAL = '+71:28:57.9' / Telescope Altitude DXT = 'LBT' / DX target fiber channel light source SXBEAMSP= 'BS1' / Name of the PFU beam splitter DXBEAMSP= 'BS1' / Name of the PFU beam splitter SXADC = 6.3 / ADC prism separation anlge at the end DXADC = 6.2 / ADC prism separation anlge at the end FIELENS = 'Red' / Field lens used FIBER = '300' / Science fiber core diameter in microns MIRROR = 'Ag' / Folding mirror coating CROSDIS = '5: 6278 - 7419' / Name of the cross-disperser TRIMSEC = '[1:10560,1401:9500]' / Image trimming section FOCUS = 6164 / Optical camera focus in microns SXPCSUM = 737 / Accumulated number of PMT counts x1000 SXPCAVR = 204 / Averaged number of PMT counts DXPCSUM = 516 / Accumulated number of PMT counts x1000 DXPCAVR = 143 / Averaged number of PMT counts RON1 = 2.526 / CCD amplifier readout noise in ADU RON2 = 2.486 / CCD amplifier readout noise in ADU RON3 = 2.219 / CCD amplifier readout noise in ADU RON4 = 3.436 / CCD amplifier readout noise in ADU RON5 = 2.443 / CCD amplifier readout noise in ADU RON6 = 2.492 / CCD amplifier readout noise in ADU RON7 = 2.635 / CCD amplifier readout noise in ADU RON8 = 2.490 / CCD amplifier readout noise in ADU RON9 = 3.452 / CCD amplifier readout noise in ADU RON10 = 2.793 / CCD amplifier readout noise in ADU RON11 = 2.618 / CCD amplifier readout noise in ADU RON12 = 2.552 / CCD amplifier readout noise in ADU RON13 = 2.642 / CCD amplifier readout noise in ADU RON14 = 2.461 / CCD amplifier readout noise in ADU RON15 = 2.321 / CCD amplifier readout noise in ADU RON16 = 2.448 / CCD amplifier readout noise in ADU IMASEC1 = '[1:1320,1:1056]' / Image section IMASEC2 = '[1321:2640,1:1056]' / Image section IMASEC3 = '[2641:3960,1:1056]' / Image section IMASEC4 = '[3961:5280,1:1056]' / Image section IMASEC5 = '[5281:6600,1:1056]' / Image section IMASEC6 = '[6601:7920,1:1056]' / Image section IMASEC7 = '[7921:9240,1:1056]' / Image section IMASEC8 = '[9241:10560,1:1056]' / Image section IMASEC9 = '[1:1320,1057:2112]' / Image section IMASEC10= '[1321:2640,1057:2112]' / Image section IMASEC11= '[2641:3960,1057:2112]' / Image section IMASEC12= '[3961:5280,1057:2112]' / Image section IMASEC13= '[5281:6600,1057:2112]' / Image section IMASEC14= '[6601:7920,1057:2112]' / Image section IMASEC15= '[7921:9240,1057:2112]' / Image section IMASEC16= '[9241:10560,1057:2112]' / Image section HISTORY Master bias image subtracted pepsir.20221104.007.bss HISTORY pepsir.20230603.009.bss / pepsir.20221027.303.mff HISTORY Spline unweighted straight fit 11 in columns HISTORY Spline unweighted clip-and-fit 6 1x1 in rows TRACE = 'pepsir.20230603.014.trace' / Echelle order definition WAS = 'pepsir.20230603.011.dxt.std' / Wavelength solution file ORDER1 = 83 / First echelle order number ORDER2 = 98 / Last echelle order number HISTORY pepsir.20230603.009.dxt.mrg / pepsir.20211010.300.dxt.mrg HISTORY Polynomial unweighted clip-and-fit 4 1x1 2D constrained HISTORY Polynomial unweighted clip-and-fit 15 1x1 2D constrained DISPROT = 0 / How to rotate image when displayed NUMFILES= 2 / Number of selected files FILE0 = 'pepsir.20230603.009.dxt.rec' / Selected file name FILE1 = 'pepsir.20230603.009.sxt.rec' / Selected file name HISTORY Weighted average OBSERVAT= 'Mt. Graham Observatory' / Name of the observatory TELESCOP= 'LBT' / Data acquisition telescope LONGITUD= '-07:19:33.38' / Longitude east positive in hours LATITUDE= '+32:42:04.71' / Geodetic latitude of telescope in degreesALTITUDE= 3221 / Altitude of the telescope in meters OBJECT = 'Gaia21dnc' / Observed object name JD-OBS = 2460098.931729641 / Julian Date of midexposure (UTC) JD-TDB = 2460098.932866639 / Barycentric Dynamical Time (TDB) JD-TCB = 2460098.933129521 / Barycentric Coordinate Time (TCB) RA = '21:39:13.57' / Apparent Right Ascension DEC = '+26:34:04.49' / Apparent Declination LST = '19:49:45.85' / Local sidereal time at midexposure HA = '22:10:32.28' / Hour angle ZD = '24:29:16.06' / Zenith distance AIRMASS = 1.0988 / Air mass PA = '-069:21:22.38' / Parallactic angle AA = 12.654 / Aberration angle, arcsec MOON = 96.372 / Distance to the Moon, degrees EQUINOX = 2023.419 / Equinox of RA/Dec EPHEMERI= 'JPL de440.bsp' / Ephemerides file name RA2000 = '21:38:10.81' / J2000 Right Ascension DE2000 = '+26:27:59.70' / J2000 Declination SSBVEL = -23246.520 / SSB velocity of the observer m/s V = 15.000 / V magnitude HISTORY Spline unweighted clip-and-fit 9 1x1 DATE = '2023-06-08T19:39:05' / UT of last modification RELEASE = '20230607' / Spectroscopic Data Systems (SDS) version END XTENSION= 'BINTABLE' / BINARY table extension BITPIX = 8 / Bits per pixel NAXIS = 2 / Number of axes NAXIS1 = 25 / Number of bytes in each row NAXIS2 = 17382 / Number of rows PCOUNT = 0 / Parameters counter GCOUNT = 1 / Group counter TFIELDS = 4 / Number of fields in the table TTYPE1 = 'Arg' / Name of the field TFORM1 = '1D' / Input format TDISP1 = 'F8.3' / Output format TINFO1 = 'double' / Type of the field TANNOT1 = 'Columns' / Field annotation TTYPE2 = 'Fun' / Name of the field TFORM2 = '1D' / Input format TDISP2 = 'F8.3' / Output format TINFO2 = 'double' / Type of the field TANNOT2 = 'Rows' / Field annotation TTYPE3 = 'Var' / Name of the field TFORM3 = '1D' / Input format TDISP3 = 'E9.3' / Output format TINFO3 = 'double' / Type of the field TANNOT3 = 'Variance' / Field annotation TTYPE4 = 'Mask' / Name of the field TFORM4 = '1L' / Input format TDISP4 = 'L1' / Output format TINFO4 = 'byte' / Type of the field TANNOT4 = 'Mask' / Field annotation EXTNAME = 'DataVector' / Name of the extension CHECKSUM= '2474904334' / Adler32 checksum of uncompressed data END @[4%?~A +$?)t9Α@[sN? ?'wU׽@[°{X?&c?'?"_?$Ŋ~@\DrE]?E+ ?$qӰ@\TSP?}+?%4]h>@\dw&&@]gYc?sϯ?#h*(v@]wxlX?Z„Z?#=@]N?(-?#tIW@] ?SbL4Q?$5@] ?[ +?&$|I@] ?uG9?#u$@]Q?V_}?#T^R@]+En?s?!}  @]-(?A?"ۺG @]Buw?n| ?-@^W&L?7?!TMUL#@^j3C? ?!/sc!@^(|z8k,?2@_8P?L t?C#4@_H40??q@_X?jQ?谷@_hCZ?P?# èk@_x*&?c9?"Uc@_jx?}?!OitZ@_ޘ?1~?!As@_GI?24H? 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Ju?E@-?"JQ?HZT@;m?gd?JV@Ibl?S?Gփ&@W%D?g,ﰺ?HúsP@e̕?X %?KL"@} ?~ ?ZW*o?@ܠ?2#% ?R9@z?*ׅ/^?XQ@ L?=t?X,?7@}q?"?[-W()7@0ѓ?i1}I7?Sλ#X@Ạ?+.J?W5$@Q_e?p.?W,9@`.?-0z@?\PH@m{?Mf 3?XkxGv@ +z8?jXH?[>u@(9?KKz?J謅X^@&?＇uc?YE@4 +~?񬙒?\F&V@BJ?I +?X{<@P?c +I?`x@^+?`?Tss@e@ly>?(7?OS  \ No newline at end of file diff --git a/examples/data/pepsir.20230603.010.dxt.nor b/examples/data/pepsir.20230603.010.dxt.nor new file mode 100644 index 0000000..bd06410 --- /dev/null +++ b/examples/data/pepsir.20230603.010.dxt.nor @@ -0,0 +1,1556 @@ +SIMPLE = T / Standard FITS format BITPIX = 8 / Bits per pixel NAXIS = 0 / Number of axes EXTEND = T / Extension is present MOSAIC = T / Image is CCD mosaic DETSIZE = '[1:10560,1:10560]' / Detector size DETECTOR= 'STA1600LN' / STA Archon controller X12-F 1.0.1028 CBIN = 1 / Binning factor in CCD columns RBIN = 5 / Binning factor in CCD rows GAIN1 = 1.550280 / CCD amplifier gain factor in e/ADU GAIN2 = 1.553919 / CCD amplifier gain factor in e/ADU GAIN3 = 1.559687 / CCD amplifier gain factor in e/ADU GAIN4 = 1.539479 / CCD amplifier gain factor in e/ADU GAIN5 = 1.562029 / CCD amplifier gain factor in e/ADU GAIN6 = 1.551208 / CCD amplifier gain factor in e/ADU GAIN7 = 1.558944 / CCD amplifier gain factor in e/ADU GAIN8 = 1.552657 / CCD amplifier gain factor in e/ADU GAIN9 = 1.522927 / CCD amplifier gain factor in e/ADU GAIN10 = 1.549798 / CCD amplifier gain factor in e/ADU GAIN11 = 1.550713 / CCD amplifier gain factor in e/ADU GAIN12 = 1.558488 / CCD amplifier gain factor in e/ADU GAIN13 = 1.550717 / CCD amplifier gain factor in e/ADU GAIN14 = 1.551005 / CCD amplifier gain factor in e/ADU GAIN15 = 1.561832 / CCD amplifier gain factor in e/ADU GAIN16 = 1.588823 / CCD amplifier gain factor in e/ADU GAINS1 = 0.132443 / CCD amplifier gain slope times 65535 GAINS2 = 0.148397 / CCD amplifier gain slope times 65535 GAINS3 = 0.126385 / CCD amplifier gain slope times 65535 GAINS4 = 0.140005 / CCD amplifier gain slope times 65535 GAINS5 = 0.129050 / CCD amplifier gain slope times 65535 GAINS6 = 0.128416 / CCD amplifier gain slope times 65535 GAINS7 = 0.131469 / CCD amplifier gain slope times 65535 GAINS8 = 0.129429 / CCD amplifier gain slope times 65535 GAINS9 = 0.120627 / CCD amplifier gain slope times 65535 GAINS10 = 0.123148 / CCD amplifier gain slope times 65535 GAINS11 = 0.127687 / CCD amplifier gain slope times 65535 GAINS12 = 0.120984 / CCD amplifier gain slope times 65535 GAINS13 = 0.125144 / CCD amplifier gain slope times 65535 GAINS14 = 0.131374 / CCD amplifier gain slope times 65535 GAINS15 = 0.130235 / CCD amplifier gain slope times 65535 GAINS16 = 0.127412 / CCD amplifier gain slope times 65535 READOUT = 16.151 / CCD camera readout time, sec TRANSFER= 0.978 / CCD image transfer time, sec ARM = 'Red' / CCD camera name CCDTEMP1= 152.9997 / CCD temperature in K at read out CCDTEMP2= 152.9352 / CCD temperature in K at read out CCDTEMP3= 153.8821 / CCD temperature in K at read out DEWATEMP= 110.0697 / Dewar temperature in K DEWARPRE= 1171.8 / Dewar pressure in nanobar DEWARATE= 6.8 / CryoCon heater rate in percent DATE-OBS= '2023-06-03' / UTC date of exposure start TIME-OBS= '11:10:42.237' / UTC time of exposure start EXPTIME = 829.746 / Effective exposure time INSTRUME= 'PEPSI' / Data acquisition instrument ORIGIN = 'SDS' / Spectroscopic Data Systems CHAPRESS= 700.9994 / Chamber pressure in mbar CHATEMPE= 19.7936 / Chamber table mean temperature in C CHAHUMID= 5.4436 / Chamber relative humidity in percent CHARAVE = 2516.766 / Chamber air-vacuum radial velocity m/s GRATING = 20.0708 / Echelle grating temperature, C IMAGETYP= 'object' / PFU@LBT exposure PID = 'Cusano' / Project ID SEEINGSX= 0.664 / SX guider image seeing in arcsec SEEINGDX= 0.651 / DX guider image seeing in arcsec LBTP = 690.800 / Air Pressure, hPa LBTT = 5.40 / Air Temperature, C LBTH = 60.1 / Air Humidity, percent LBTW = 2.8 / Air WindSpeed, m/s LBTRA = '21:38:10.8' / Telescope Right Ascension LBTDE = '+26:27:59.7' / Telescope Declination LBTAZ = '-062:28:19.8' / Telescope Azimuth LBTAL = '+77:58:49.3' / Telescope Altitude DXT = 'LBT' / DX target fiber channel light source SXBEAMSP= 'BS1' / Name of the PFU beam splitter DXBEAMSP= 'BS1' / Name of the PFU beam splitter SXADC = 4.1 / ADC prism separation anlge at the end DXADC = 4.1 / ADC prism separation anlge at the end FIELENS = 'Red' / Field lens used FIBER = '300' / Science fiber core diameter in microns MIRROR = 'Ag' / Folding mirror coating CROSDIS = '6: 7419 - 9067' / Name of the cross-disperser TRIMSEC = '[1:10560,1501:9600]' / Image trimming section FOCUS = 6164 / Optical camera focus in microns SXPCSUM = 181 / Accumulated number of PMT counts x1000 SXPCAVR = 219 / Averaged number of PMT counts DXPCSUM = 147 / Accumulated number of PMT counts x1000 DXPCAVR = 177 / Averaged number of PMT counts RON1 = 2.518 / CCD amplifier readout noise in ADU RON2 = 2.410 / CCD amplifier readout noise in ADU RON3 = 2.365 / CCD amplifier readout noise in ADU RON4 = 3.208 / CCD amplifier readout noise in ADU RON5 = 2.134 / CCD amplifier readout noise in ADU RON6 = 2.338 / CCD amplifier readout noise in ADU RON7 = 2.353 / CCD amplifier readout noise in ADU RON8 = 2.456 / CCD amplifier readout noise in ADU RON9 = 3.476 / CCD amplifier readout noise in ADU RON10 = 2.797 / CCD amplifier readout noise in ADU RON11 = 2.594 / CCD amplifier readout noise in ADU RON12 = 2.471 / CCD amplifier readout noise in ADU RON13 = 2.627 / CCD amplifier readout noise in ADU RON14 = 2.620 / CCD amplifier readout noise in ADU RON15 = 2.583 / CCD amplifier readout noise in ADU RON16 = 2.427 / CCD amplifier readout noise in ADU IMASEC1 = '[1:1320,1:1056]' / Image section IMASEC2 = '[1321:2640,1:1056]' / Image section IMASEC3 = '[2641:3960,1:1056]' / Image section IMASEC4 = '[3961:5280,1:1056]' / Image section IMASEC5 = '[5281:6600,1:1056]' / Image section IMASEC6 = '[6601:7920,1:1056]' / Image section IMASEC7 = '[7921:9240,1:1056]' / Image section IMASEC8 = '[9241:10560,1:1056]' / Image section IMASEC9 = '[1:1320,1057:2112]' / Image section IMASEC10= '[1321:2640,1057:2112]' / Image section IMASEC11= '[2641:3960,1057:2112]' / Image section IMASEC12= '[3961:5280,1057:2112]' / Image section IMASEC13= '[5281:6600,1057:2112]' / Image section IMASEC14= '[6601:7920,1057:2112]' / Image section IMASEC15= '[7921:9240,1057:2112]' / Image section IMASEC16= '[9241:10560,1057:2112]' / Image section HISTORY Master bias image subtracted pepsir.20221104.007.bss HISTORY pepsir.20230603.010.bss / pepsir.20221028.369.mff HISTORY Spline unweighted straight fit 11 in columns HISTORY Spline unweighted clip-and-fit 6 1x1 in rows TRACE = 'pepsir.20230605.038.trace' / Echelle order definition WAS = 'pepsir.20230605.027.dxt.std' / Wavelength solution file ORDER1 = 68 / First echelle order number ORDER2 = 83 / Last echelle order number HISTORY pepsir.20230603.010.dxt.mrg / pepsir.20211010.900.dxt.mrg HISTORY Polynomial unweighted clip-and-fit 4 1x1 2D constrained HISTORY Polynomial unweighted clip-and-fit 15 1x1 2D constrained DISPROT = 0 / How to rotate image when displayed NUMFILES= 2 / Number of selected files FILE0 = 'pepsir.20230603.010.dxt.rec' / Selected file name FILE1 = 'pepsir.20230603.010.sxt.rec' / Selected file name HISTORY Weighted average OBSERVAT= 'Mt. Graham Observatory' / Name of the observatory TELESCOP= 'LBT' / Data acquisition telescope LONGITUD= '-07:19:33.38' / Longitude east positive in hours LATITUDE= '+32:42:04.71' / Geodetic latitude of telescope in degreesALTITUDE= 3221 / Altitude of the telescope in meters OBJECT = 'Gaia21dnc' / Observed object name JD-OBS = 2460098.970568403 / Julian Date of midexposure (UTC) JD-TDB = 2460098.971708406 / Barycentric Dynamical Time (TDB) JD-TCB = 2460098.971971289 / Barycentric Coordinate Time (TCB) RA = '21:39:13.57' / Apparent Right Ascension DEC = '+26:34:04.52' / Apparent Declination LST = '20:45:50.70' / Local sidereal time at midexposure HA = '23:06:37.13' / Hour angle ZD = '13:06:21.68' / Zenith distance AIRMASS = 1.0267 / Air mass PA = '-059:38:24.46' / Parallactic angle AA = 12.644 / Aberration angle, arcsec MOON = 96.013 / Distance to the Moon, degrees EQUINOX = 2023.419 / Equinox of RA/Dec EPHEMERI= 'JPL de440.bsp' / Ephemerides file name RA2000 = '21:38:10.81' / J2000 Right Ascension DE2000 = '+26:27:59.70' / J2000 Declination SSBVEL = -23165.062 / SSB velocity of the observer m/s V = 15.000 / V magnitude HISTORY Spline unweighted clip-and-fit 8 1x1 DATE = '2023-06-08T19:39:21' / UT of last modification RELEASE = '20230607' / Spectroscopic Data Systems (SDS) version END XTENSION= 'BINTABLE' / BINARY table extension BITPIX = 8 / Bits per pixel NAXIS = 2 / Number of axes NAXIS1 = 25 / Number of bytes in each row NAXIS2 = 21333 / Number of rows PCOUNT = 0 / Parameters counter GCOUNT = 1 / Group counter TFIELDS = 4 / Number of fields in the table TTYPE1 = 'Arg' / Name of the field TFORM1 = '1D' / Input format TDISP1 = 'F8.3' / Output format TINFO1 = 'double' / Type of the field TANNOT1 = 'Columns' / Field annotation TTYPE2 = 'Fun' / Name of the field TFORM2 = '1D' / Input format TDISP2 = 'F8.3' / Output format TINFO2 = 'double' / Type of the field TANNOT2 = 'Rows' / Field annotation TTYPE3 = 'Var' / Name of the field TFORM3 = '1D' / Input format TDISP3 = 'E9.3' / Output format TINFO3 = 'double' / Type of the field TANNOT3 = 'Variance' / Field annotation TTYPE4 = 'Mask' / Name of the field TFORM4 = '1L' / Input format TDISP4 = 'L1' / Output format TINFO4 = 'byte' / Type of the field TANNOT4 = 'Mask' / Field annotation EXTNAME = 'DataVector' / Name of the extension CHECKSUM= '2470579985' / Adler32 checksum of uncompressed data END @'|Ӫ?sG?Z,@;9 X?I?WWeF@N>z?=Q?VmO@b?h1?P @u)N?kr4~5"?Ut!@ +?dz2?Tt@ j?!,?S@?BB?W5<@Ád?@ ?WO1@Ax?R?Pw?f@t?>&!?PJe6@?~g{ +?S;@bx?ZS?BSʟŲ@$?+ۈBI?UKI@8K^g?82g ?PR@K?pA?Q">xB@_.\?7u;?[\@r ?,W?W#>( +@ ?0e^1?Qc +z5@x.g?]2?UrGbj%@y? +EA?S1@M?I-?Ol'@Ӷ}G??O(vc'@S?v^f?K;ݘ@?#Z?U&7?Uv+iQ@ ;?!^ ?Wm} @!K=?VDx?UFy@4(?§?TA]&@H m]?'X{?Vj@[lyl?ޜC?S' @n(q?Zכ?T.g,'9@&z]?%?U-5@oA@k?`I9.@Y@)~^?d$@3DJo@Ō?]NE@ϊ#? 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Imports\n", + "\n", + "We import the core Spyctres interfaces, a few plotting helpers, and standard scientific Python tools.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d7f64e07", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using Spyctres from:\n", + "/home/Tux/ytsapras/Installed_Programs/Spyctres-dev/Spyctres/__init__.py\n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "import sys\n", + "import warnings\n", + "\n", + "repo_root = None\n", + "for candidate in [Path.cwd().resolve(), *Path.cwd().resolve().parents]:\n", + " if (candidate / \"Spyctres\").exists() and (candidate / \"setup.py\").exists():\n", + " repo_root = candidate\n", + " break\n", + "if repo_root is not None and str(repo_root) not in sys.path:\n", + " sys.path.insert(0, str(repo_root))\n", + "\n", + "# This will suppress some obsolete warnings\n", + "warnings.filterwarnings(\n", + " \"ignore\",\n", + " message=r\"pkg_resources is deprecated as an API.*\",\n", + " category=UserWarning,\n", + " module=r\"pysynphot.*\",\n", + ")\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import Spyctres\n", + "from Spyctres.config import load_user_config, get_config_value, resolve_setting\n", + "from Spyctres.io import read_spectrum, make_padded_window_segments\n", + "from Spyctres.phoenix import PhoenixLibrary\n", + "from Spyctres.waveutils import convert_wavelength_medium\n", + "from Spyctres.fitting import (\n", + " fit_phoenix_full_spectrum,\n", + " build_effective_fit_mask,\n", + " reconstruct_phoenix_legendre_models_for_segments,\n", + ")\n", + "from Spyctres.plotting import plot_binned_spectra, plot_full_spectrum_fit\n", + "\n", + "print(\"Using Spyctres from:\")\n", + "print(Spyctres.__file__)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fedf5571", + "metadata": {}, + "source": [ + "## 2. Locate the PHOENIX templates and the example data\n", + "\n", + "The PHOENIX path is resolved from the user config or the `SPYCTRES_PHOENIX_DIR` environment variable.\n", + "\n", + "The notebook can be run either from the repo root or from the `examples/` directory.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c3958b99", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spectrum path: /home/Tux/ytsapras/Installed_Programs/Spyctres-dev/examples/data/TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_UVB.fits\n", + "PHOENIX dir : /home/Tux/ytsapras/Installed_Programs/astroARIADNE/Models_Dir/PHOENIXv2\n" + ] + } + ], + "source": [ + "config = load_user_config()\n", + "phoenix_dir_cfg = get_config_value(config, \"paths\", \"phoenix_dir\", default=None)\n", + "phoenix_dir = resolve_setting(\n", + " cli_value=None,\n", + " env_var_name=\"SPYCTRES_PHOENIX_DIR\",\n", + " config_value=phoenix_dir_cfg,\n", + " default=None,\n", + ")\n", + "if phoenix_dir is None:\n", + " raise RuntimeError(\n", + " \"No PHOENIX directory found. Set SPYCTRES_PHOENIX_DIR or \"\n", + " \"[paths].phoenix_dir in ~/.config/spyctres/config.toml.\"\n", + " )\n", + "\n", + "cwd = Path.cwd().resolve()\n", + "if (cwd / \"examples\" / \"data\").exists():\n", + " data_dir = cwd / \"examples\" / \"data\"\n", + "elif (cwd / \"data\").exists():\n", + " data_dir = cwd / \"data\"\n", + "else:\n", + " raise FileNotFoundError(\n", + " \"Could not find the example data directory. Run this notebook from the repo root \"\n", + " \"or from the examples/ directory.\"\n", + " )\n", + "\n", + "spectrum_path = data_dir / \"TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_UVB.fits\"\n", + "\n", + "print(\"Spectrum path:\", spectrum_path)\n", + "print(\"PHOENIX dir :\", phoenix_dir)\n" + ] + }, + { + "cell_type": "markdown", + "id": "820918aa", + "metadata": {}, + "source": [ + "## 3. Read the UVB spectrum and inspect the metadata\n", + "\n", + "The X-SHOOTER reader stores basic metadata such as the arm, wavelength medium, frame, and an inferred nominal resolving power.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f7784767", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name : TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_UVB.fits\n", + "Object : Gaia21ccu\n", + "Instrument : XSHOOTER\n", + "Arm : UVB\n", + "Wave medium : air\n", + "Wave frame : topocentric\n", + "R metadata : 5400.0\n", + "Barycorr [km/s] : 13.5272193938759\n", + "Pixels : 12854\n", + "Wave range [Å] : 2989.2000000000003 5559.8\n" + ] + } + ], + "source": [ + "seg0 = read_spectrum(str(spectrum_path), instrument=\"xshooter\")\n", + "\n", + "print(\"Name :\", seg0.name)\n", + "print(\"Object :\", seg0.meta.get(\"object\"))\n", + "print(\"Instrument :\", seg0.meta.get(\"instrument\"))\n", + "print(\"Arm :\", seg0.meta.get(\"arm\"))\n", + "print(\"Wave medium :\", seg0.wave_medium)\n", + "print(\"Wave frame :\", seg0.wave_frame)\n", + "print(\"R metadata :\", seg0.meta.get(\"resolution_R\"))\n", + "print(\"Barycorr [km/s] :\", seg0.meta.get(\"barycorr_kms\"))\n", + "print(\"Pixels :\", len(seg0.wave))\n", + "print(\"Wave range [Å] :\", float(np.min(seg0.wave)), float(np.max(seg0.wave)))\n" + ] + }, + { + "cell_type": "markdown", + "id": "49c97a4b", + "metadata": {}, + "source": [ + "## 4. Quick look at the spectrum\n", + "\n", + "We use a median-binned view here so the overall shape and the main absorption lines are easier to see.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "622f0595", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit_windows = [\n", + " (\"Hdelta\", 3980.0, 4220.0),\n", + " (\"Hgamma\", 4220.0, 4480.0),\n", + " (\"Hbeta\", 4700.0, 5020.0),\n", + "]\n", + "\n", + "fig, ax = plot_binned_spectra(\n", + " spectra=[seg0],\n", + " nbins=700,\n", + " title=\"Gaia21ccu X-SHOOTER UVB quick look\",\n", + " highlight_regions=[(w0, w1) for _, w0, w1 in fit_windows],\n", + ")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "f9c4639c", + "metadata": {}, + "source": [ + "## 5. Choose the wavelength windows\n", + "\n", + "For this example we focus on three broad hydrogen lines in the UVB arm: Hdelta, Hgamma, and Hbeta.\n", + "\n", + "Each fit window will later be given a small padding so the forward model has a little extra support on both sides.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "95003b7c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Clipped segment:\n", + " name : TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_UVB.fits_fitwin\n", + " pixels : 7601\n", + " wave range : 3980.0 5500.0\n", + "\n", + "Per-window segments:\n", + " Hdelta : pixels=1226 support=[3980.0, 4225.0] Å fit_pixels=974\n", + " Hgamma : pixels=1351 support=[4215.0, 4485.0] Å fit_pixels=1298\n", + " Hbeta : pixels=1651 support=[4695.0, 5025.0] Å fit_pixels=1585\n" + ] + } + ], + "source": [ + "wmin = 3980.0\n", + "wmax = 5500.0\n", + "window_pad = 5.0\n", + "\n", + "seg_clip = seg0.window(\n", + " wmin=wmin,\n", + " wmax=wmax,\n", + " clip_left=0.0,\n", + " clip_right=0.0,\n", + " name_suffix=\"fitwin\",\n", + ")\n", + "\n", + "segments = make_padded_window_segments(\n", + " seg_clip,\n", + " fit_windows,\n", + " pad=window_pad,\n", + ")\n", + "\n", + "for seg_i, (label, _w0, _w1) in zip(segments, fit_windows):\n", + " seg_i.name = label\n", + "\n", + "print(\"Clipped segment:\")\n", + "print(\" name :\", seg_clip.name)\n", + "print(\" pixels :\", len(seg_clip.wave))\n", + "print(\" wave range :\", float(np.min(seg_clip.wave)), float(np.max(seg_clip.wave)))\n", + "\n", + "print(\"\\nPer-window segments:\")\n", + "for s in segments:\n", + " print(\n", + " f\" {s.name:>6s} : \"\n", + " f\"pixels={len(s.wave):4d} \"\n", + " f\"support=[{float(np.min(s.wave)):.1f}, {float(np.max(s.wave)):.1f}] Å \"\n", + " f\"fit_pixels={int(np.sum(s.mask))}\"\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "id": "a3b85970", + "metadata": {}, + "source": [ + "## 6. Exclude the very line centers\n", + "\n", + "For a simple first fit, it is often useful to exclude a narrow region around the center of each Balmer line and fit mainly the wings.\n", + "\n", + "Here we use a symmetric half-width of 6 Å around each line center.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6566d694", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Per-window used pixels:\n", + " Hdelta: 916 / 1226\n", + " Hgamma: 1238 / 1351\n", + " Hbeta: 1525 / 1651\n", + "\n", + "Total used pixels: 3679\n" + ] + } + ], + "source": [ + "from Spyctres.recipes import make_balmer_core_exclude_mask\n", + "\n", + "# For the core_mask_halfwidth, try 6, 8, 10, 12 Angstrom as a robustness check. \n", + "# Stable parameters across this range are more trustworthy than a single fit.\n", + "core_mask_halfwidth = 6.0\n", + "exclude_mask = make_balmer_core_exclude_mask(\n", + " core_halfwidth=core_mask_halfwidth,\n", + " wave_medium=segments[0].wave_medium,\n", + ")\n", + "\n", + "used_masks_plot = [\n", + " build_effective_fit_mask(seg, exclude_mask=exclude_mask)\n", + " for seg in segments\n", + "]\n", + "\n", + "print(\"Per-window used pixels:\")\n", + "for seg_i, mask_i in zip(segments, used_masks_plot):\n", + " print(f\" {seg_i.name:>6s}: {int(np.sum(mask_i))} / {len(seg_i.wave)}\")\n", + "print(\"\\nTotal used pixels:\", int(sum(np.sum(m) for m in used_masks_plot)))\n" + ] + }, + { + "cell_type": "markdown", + "id": "5795efb9", + "metadata": {}, + "source": [ + "## 7. Define the PHOENIX grid\n", + "\n", + "We do not use the full installed PHOENIX library for this example. Instead we choose a smaller subgrid around the part of parameter space that looks reasonable for this spectrum.\n", + "\n", + "This keeps the notebook faster and makes the fit easier to interpret.\n", + "\n", + "For real work, start broad enough that the solution is not pinned to a grid edge, then refine.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "93734ddf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Installed Teff range : 2300.0 12000.0\n", + "Installed [Fe/H] range: -4.0 1.0\n", + "Installed logg range : 0.0 6.0\n", + "\n", + "Teff grid : [ 9000. 9200. 9400. 9600. 9800. 10000. 10200. 10400. 10600. 10800.\n", + " 11000. 11200. 11400. 11600.]\n", + "FeH grid : [-2. -1.5 -1. -0.5 -0. ]\n", + "logg grid : [2. 2.5 3. 3.5 4. 4.5]\n" + ] + } + ], + "source": [ + "phoenix_lib = PhoenixLibrary(phoenix_dir, verbose=True)\n", + "\n", + "teff_avail, feh_avail, logg_avail = phoenix_lib.available_axes()\n", + "print(\"Installed Teff range :\", float(np.min(teff_avail)), float(np.max(teff_avail)))\n", + "print(\"Installed [Fe/H] range:\", float(np.min(feh_avail)), float(np.max(feh_avail)))\n", + "print(\"Installed logg range :\", float(np.min(logg_avail)), float(np.max(logg_avail)))\n", + "\n", + "teff_grid_fit = teff_avail[(teff_avail >= 9000.0) & (teff_avail <= 11600.0)]\n", + "feh_grid_fit = feh_avail[(feh_avail >= -2.0) & (feh_avail <= 0.0)]\n", + "logg_grid_fit = logg_avail[(logg_avail >= 2.0) & (logg_avail <= 4.5)]\n", + "\n", + "print(\"\\nTeff grid :\", teff_grid_fit)\n", + "print(\"FeH grid :\", feh_grid_fit)\n", + "print(\"logg grid :\", logg_grid_fit)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3e7a0e16", + "metadata": {}, + "source": [ + "## 8. Run the fit\n", + "\n", + "The fit solves for:\n", + "- effective temperature `Teff`\n", + "- metallicity `[Fe/H]`\n", + "- surface gravity `logg`\n", + "- radial velocity `RV`\n", + "\n", + "It also solves a low-order multiplicative continuum correction for each segment during the fit.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7998d1d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R used : 5400.0\n", + "rv_bary_kms used : 0.0\n", + "Initial guess p0 : (10000.0, -0.5, 3.0, -5.0)\n", + "Loaded PHOENIX cache from /tmp/spyctres_example_xshooter_cache.npz\n", + "RV init grid best: -22.5\n", + " Iteration Total nfev Cost Cost reduction Step norm Optimality \n", + " 0 1 1.2926e+04 1.34e+04 \n", + " 1 2 1.1005e+04 1.92e+03 2.95e+02 3.33e+03 \n", + " 2 3 1.0752e+04 2.53e+02 5.63e+01 9.39e+02 \n", + " 3 4 1.0732e+04 1.95e+01 2.25e+02 1.97e+03 \n", + " 4 5 1.0722e+04 9.75e+00 9.46e+01 1.24e+03 \n", + " 5 6 1.0720e+04 2.04e+00 2.09e+02 5.55e+02 \n", + " 6 7 1.0719e+04 1.57e+00 4.89e+00 3.71e+02 \n", + " 7 8 1.0719e+04 2.99e-01 3.20e+01 2.37e+02 \n", + " 8 10 1.0718e+04 3.32e-01 1.24e+01 1.98e+03 \n", + " 9 13 1.0718e+04 9.02e-02 2.85e-01 3.92e+01 \n", + " 10 14 1.0718e+04 6.77e-03 2.64e-01 2.89e+01 \n", + " 11 15 1.0718e+04 7.08e-03 5.94e-01 1.95e+03 \n", + " 12 17 1.0718e+04 3.45e-03 7.12e-02 1.87e+03 \n", + " 13 19 1.0718e+04 1.73e-05 4.35e-03 1.95e+03 \n", + " 14 20 1.0718e+04 2.40e-06 1.11e-03 1.95e+03 \n", + " 15 22 1.0718e+04 0.00e+00 0.00e+00 1.95e+03 \n", + "`xtol` termination condition is satisfied.\n", + "Function evaluations 22, initial cost 1.2926e+04, final cost 1.0718e+04, first-order optimality 1.95e+03.\n", + "\n", + "Fit result summary:\n", + " teff : 10227.704640203781\n", + " feh : -0.46812545976826697\n", + " logg : 2.7302403389613934\n", + " rv_kms : -6.548656961933615\n", + " chi2 : 21436.237079173436\n", + " dof : 3675\n", + "chi2_red : 5.832989681407738\n", + " success : True\n", + " message : `xtol` termination condition is satisfied.\n" + ] + } + ], + "source": [ + "R = seg0.meta.get(\"resolution_R\", None)\n", + "# For simplicity, this example does not apply an additional barycentric correction during\n", + "# the fit, even though the reader reports one in the metadata.\n", + "# We set rv_bary_kms = 0.0 because it keeps the example focused on spectral classification. \n", + "# The fitted RV should therefore be read as an internal alignment parameter, \n", + "# not a final barycentric radial velocity.\n", + "rv_bary_kms = 0.0\n", + "# For a barycentric RV-oriented run, use:\n", + "# rv_bary_kms = seg0.meta.get(\"barycorr_kms\", 0.0)\n", + "p0 = (10000.0, -0.5, 3.0, -5.0)\n", + "\n", + "print(\"R used :\", R)\n", + "print(\"rv_bary_kms used :\", rv_bary_kms)\n", + "print(\"Initial guess p0 :\", p0)\n", + "\n", + "result = fit_phoenix_full_spectrum(\n", + " segments=segments,\n", + " phoenix_lib=phoenix_lib,\n", + " p0=p0,\n", + " exclude_mask=exclude_mask,\n", + " rv_bary_kms=rv_bary_kms,\n", + " R=R,\n", + " forward_model=\"native_interp\",\n", + " teff_grid=teff_grid_fit,\n", + " feh_grid=feh_grid_fit,\n", + " logg_grid=logg_grid_fit,\n", + " cache_path=\"/tmp/spyctres_example_xshooter_cache.npz\",\n", + " verbose=1,\n", + " max_nfev=300,\n", + ")\n", + "\n", + "print(\"\\nFit result summary:\")\n", + "for key in [\"teff\", \"feh\", \"logg\", \"rv_kms\", \"chi2\", \"dof\", \"chi2_red\", \"success\", \"message\"]:\n", + " print(f\"{key:>8s} : {result[key]}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "da6f1638", + "metadata": {}, + "source": [ + "## 9. Reconstruct and inspect the fitted model\n", + "\n", + "This is the main diagnostic step. The plots show the data, the fitted model, and which pixels were actually used in the fit.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "55906a19", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_list, coeffs_list, used_masks, excluded_masks = reconstruct_phoenix_legendre_models_for_segments(\n", + " segments=segments,\n", + " phoenix_lib=phoenix_lib,\n", + " fit_result=result,\n", + " exclude_mask=exclude_mask,\n", + " rv_bary_kms=rv_bary_kms,\n", + " R=R,\n", + " forward_model=\"native_interp\",\n", + ")\n", + "\n", + "for seg_i, model_i, used_i, excl_i in zip(segments, model_list, used_masks, excluded_masks):\n", + " title = (\n", + " f\"{seg_i.name} \"\n", + " f\"Teff={result['teff']:.0f} K \"\n", + " f\"[Fe/H]={result['feh']:.2f} \"\n", + " f\"logg={result['logg']:.2f} \"\n", + " f\"RV={result['rv_kms']:.1f} km/s \"\n", + " f\"chi2_red={result['chi2_red']:.2f}\"\n", + " )\n", + " fig, axes = plot_full_spectrum_fit(\n", + " wave=seg_i.wave,\n", + " flux=seg_i.flux,\n", + " err=seg_i.err,\n", + " model=model_i,\n", + " used_mask=used_i,\n", + " excluded_mask=excl_i,\n", + " title=title,\n", + " line_groups=[\"balmer\"],\n", + " )\n", + "\n", + " ax_top = axes[0]\n", + " y_min = min(np.nanmin(seg_i.flux), np.nanmin(model_i))\n", + " y_max = max(np.nanmax(seg_i.flux), np.nanmax(model_i))\n", + " y_pad = 0.05 * (y_max - y_min)\n", + " ax_top.set_ylim(y_min - y_pad, y_max + y_pad)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8d0a9df9-0380-49cf-b82b-2a633ae7baf5", + "metadata": {}, + "source": [ + "The shaded region is excluded from the fit. A mismatch inside the shaded core should not be interpreted as a failed fit; the relevant comparison is mainly in the unshaded wings." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8416736a-1eb1-42f2-9cd4-cb3e73b6dd04", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "chi2_red: 5.832989681407738\n", + "edge flags: not reported\n", + "line-window residuals should be inspected visually before adopting the classification.\n" + ] + } + ], + "source": [ + "# Assess fit quality\n", + "print(\"chi2_red:\", result[\"chi2_red\"])\n", + "edge_flags = result.get(\"edge_flags\", {})\n", + "print(\"edge flags:\", edge_flags if edge_flags else \"not reported\")\n", + "print(\"line-window residuals should be inspected visually before adopting the classification.\")" + ] + }, + { + "cell_type": "markdown", + "id": "8878717e", + "metadata": {}, + "source": [ + "## 10. Summarize the result\n", + "\n", + "This is a compact text summary that is easy to copy into notes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "feb307d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generic PHOENIX fit summary:\n", + " Teff [K] : 10227.705\n", + " [Fe/H] : -0.468\n", + " logg : 2.730\n", + " RV [km/s] : -6.549\n", + " Reduced chi^2 : 5.833\n", + "\n", + "Interpretation:\n", + "The spectrum is matched by a model with Teff 10228 K, [Fe/H] -0.47, logg 2.73, and RV -6.5 km/s.\n", + "This should be read as a first model-based classification from a simple workflow, not as a final precision measurement.\n" + ] + } + ], + "source": [ + "fit_summary = {\n", + " \"Teff [K]\": result[\"teff\"],\n", + " \"[Fe/H]\": result[\"feh\"],\n", + " \"logg\": result[\"logg\"],\n", + " \"RV [km/s]\": result[\"rv_kms\"],\n", + " \"Reduced chi^2\": result[\"chi2_red\"],\n", + "}\n", + "\n", + "print(\"Generic PHOENIX fit summary:\")\n", + "for key, value in fit_summary.items():\n", + " if isinstance(value, (int, float, np.floating)):\n", + " print(f\" {key:>15s} : {value:.3f}\")\n", + " else:\n", + " print(f\" {key:>15s} : {value}\")\n", + "\n", + "print(\"\\nInterpretation:\")\n", + "print(\n", + " f\"The spectrum is matched by a model with Teff {result['teff']:.0f} K, \"\n", + " f\"[Fe/H] {result['feh']:.2f}, logg {result['logg']:.2f}, \"\n", + " f\"and RV {result['rv_kms']:.1f} km/s.\"\n", + ")\n", + "print(\n", + " \"This should be read as a first model-based classification from a simple workflow, \"\n", + " \"not as a final precision measurement.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c5371f39", + "metadata": {}, + "source": [ + "## 11. What to try next\n", + "\n", + "A few natural follow-up tests are:\n", + "- revise the fitted wavelength windows\n", + "- try a different Balmer-core mask width\n", + "- test different PHOENIX subgrids\n", + "- inspect each line separately\n", + "- compare different continuum treatments\n", + "- revisit the assumed resolving power or wavelength handling if the residuals suggest a mismatch\n", + "\n", + "For many real spectra, fitting is iterative: inspect the residuals, adjust the modeling choices, and fit again.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (spyctres2)", + "language": "python", + "name": "spyctres2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/pepsi_legacy_linefit_validation.ipynb b/examples/pepsi_legacy_linefit_validation.ipynb new file mode 100644 index 0000000..47dcde6 --- /dev/null +++ b/examples/pepsi_legacy_linefit_validation.ipynb @@ -0,0 +1,1345 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "19e799f4-0d55-465a-a9dc-e2db9ed84c39", + "metadata": {}, + "source": [ + "# PEPSI legacy line-window validation with PHOENIX\n", + "\n", + "This notebook validates a PEPSI line-window workflow using the same multi-segment infrastructure used elsewhere in Spyctres.\n", + "\n", + "The shared infrastructure is:\n", + "\n", + "- `SpectrumSegment` for individual spectral windows\n", + "- `SpectrumCollection` for a joint fit over several windows\n", + "- one shared PHOENIX parameter set across all selected segments\n", + "- native-grid PHOENIX forward modelling\n", + "- wavelength-medium handling\n", + "- instrumental broadening\n", + "- PHOENIX interpolation caching\n", + "\n", + "The fit is still a multi-segment PHOENIX fit: several `SpectrumSegment`s are combined in a `SpectrumCollection`, one shared set of stellar parameters is fitted across all windows, and the PHOENIX model is evaluated through the same native-grid forward-model machinery used elsewhere in Spyctres.\n", + "\n", + "The PEPSI-specific part is the local normalization used inside each narrow line window. For this validation case, each model window is normalized by its local maximum before comparison to the normalized PEPSI data, with a fitted error-scale nuisance parameter. This matches the legacy PEPSI line-window workflow more closely than a generic continuum correction over broader spectral regions.\n", + "\n", + "This notebook is therefore not a separate general classification recipe. It is a validation notebook showing that the same multi-segment infrastructure can support the PEPSI legacy line-window analysis in a modular way." + ] + }, + { + "cell_type": "markdown", + "id": "b9028700-04e9-44e5-a460-3f2ac64221bd", + "metadata": {}, + "source": [ + "## Workflow\n", + "\n", + "1. Locate the PHOENIX templates and PEPSI example spectra.\n", + "2. Read the PEPSI files into `SpectrumSegment` objects.\n", + "3. Build PEPSI legacy line-window segments.\n", + "4. Wrap those windows in a `SpectrumCollection`.\n", + "5. Build or load the PHOENIX interpolation cache.\n", + "6. Define a compact notebook-level objective for the legacy PEPSI likelihood.\n", + "7. Run a fast validation fit.\n", + "8. Plot the fitted line windows and inspect the result." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "33d8ce29-cf05-4902-9cc1-d67c15eb4545", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\n", + " \"ignore\",\n", + " message=r\"pkg_resources is deprecated as an API.*\",\n", + " category=UserWarning,\n", + " module=r\"pysynphot.*\",\n", + ")\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.optimize import minimize" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a01db7ce-b2e8-4837-94ea-c977b3e8eae5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using repo root: /home/Tux/ytsapras/Installed_Programs/Spyctres-dev\n" + ] + } + ], + "source": [ + "# Make sure the notebook imports the local development checkout, not an older\n", + "# installed Spyctres package from site-packages.\n", + "\n", + "import sys\n", + "from pathlib import Path\n", + "\n", + "cwd = Path.cwd().resolve()\n", + "\n", + "if (cwd / \"Spyctres\").exists():\n", + " repo_root = cwd\n", + "elif (cwd.parent / \"Spyctres\").exists():\n", + " repo_root = cwd.parent\n", + "else:\n", + " # Adjust this only if you run the notebook from somewhere unusual.\n", + " repo_root = Path(\"~/Installed_Programs/Spyctres-dev\").expanduser().resolve()\n", + "\n", + "if str(repo_root) not in sys.path:\n", + " sys.path.insert(0, str(repo_root))\n", + "\n", + "print(\"Using repo root:\", repo_root)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "570b59a8-8831-4d7d-b34f-0c1521355f91", + "metadata": {}, + "outputs": [], + "source": [ + "from Spyctres import Spyctres\n", + "from Spyctres.config import load_user_config, get_config_value, resolve_setting\n", + "from Spyctres.io import read_spectrum, SpectrumCollection\n", + "from Spyctres.phoenix import PhoenixLibrary\n", + "from Spyctres.plotting import plot_full_spectrum_fit\n", + "\n", + "from Spyctres.recipes import (\n", + " build_pepsi_legacy_segments,\n", + " make_pepsi_legacy_cache_support_segments,\n", + " ensure_phoenix_native_interpolator_for_segments,\n", + " pick_grid_range,\n", + " evaluate_pepsi_legacy_max_models,\n", + " pepsi_legacy_max_likelihood_terms,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5a01a116-a7dc-483e-bc98-7479561637ee", + "metadata": {}, + "source": [ + "## Locate PHOENIX and the PEPSI example spectra\n", + "\n", + "The PHOENIX template directory is read from the same configuration system used by the scripts:\n", + "\n", + "1. `SPYCTRES_PHOENIX_DIR`\n", + "2. `~/.config/spyctres/config.toml`\n", + "\n", + "The notebook can be run either from the repository root or from the `examples/` directory." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "05b89fb4-d224-4650-9046-543575c0af94", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PHOENIX dir: /home/Tux/ytsapras/Installed_Programs/astroARIADNE/Models_Dir/PHOENIXv2\n", + "Data dir : /home/Tux/ytsapras/Installed_Programs/Spyctres-dev/examples/data\n", + "PEPSI files used in this validation:\n", + " pepsir.20230603.009.dxt.nor\n", + " pepsir.20230603.010.dxt.nor\n", + "Blue PEPSI file available but not used in the default red-window validation:\n", + " pepsib.20230603.014.dxt.nor\n" + ] + } + ], + "source": [ + "# Locate the local PHOENIX template directory.\n", + "\n", + "config = load_user_config()\n", + "phoenix_dir_cfg = get_config_value(config, \"paths\", \"phoenix_dir\", default=None)\n", + "\n", + "phoenix_dir = resolve_setting(\n", + " None,\n", + " env_var_name=\"SPYCTRES_PHOENIX_DIR\",\n", + " config_value=phoenix_dir_cfg,\n", + " default=None,\n", + ")\n", + "\n", + "if phoenix_dir is None:\n", + " raise RuntimeError(\n", + " \"No PHOENIX directory found. Set SPYCTRES_PHOENIX_DIR or configure \"\n", + " \"~/.config/spyctres/config.toml.\"\n", + " )\n", + "\n", + "phoenix_dir = Path(phoenix_dir).expanduser().resolve()\n", + "\n", + "if not phoenix_dir.exists():\n", + " raise FileNotFoundError(f\"PHOENIX directory does not exist: {phoenix_dir}\")\n", + "\n", + "# Locate example data. This works if the notebook is run from the repo root\n", + "# or from the examples/ directory.\n", + "\n", + "cwd = Path.cwd().resolve()\n", + "\n", + "if (cwd / \"examples\" / \"data\").exists():\n", + " data_dir = cwd / \"examples\" / \"data\"\n", + "elif (cwd / \"data\").exists():\n", + " data_dir = cwd / \"data\"\n", + "else:\n", + " raise FileNotFoundError(\n", + " \"Could not find the example data directory. Run this notebook from \"\n", + " \"the repo root or from the examples/ directory.\"\n", + " )\n", + "\n", + "pepsi_blue_path = data_dir / \"pepsib.20230603.014.dxt.nor\"\n", + "pepsi_red1_path = data_dir / \"pepsir.20230603.009.dxt.nor\"\n", + "pepsi_red2_path = data_dir / \"pepsir.20230603.010.dxt.nor\"\n", + "\n", + "pepsi_paths = [pepsi_red1_path, pepsi_red2_path]\n", + "\n", + "for path in pepsi_paths:\n", + " if not path.exists():\n", + " raise FileNotFoundError(path)\n", + "\n", + "print(\"PHOENIX dir:\", phoenix_dir)\n", + "print(\"Data dir :\", data_dir)\n", + "print(\"PEPSI files used in this validation:\")\n", + "for path in pepsi_paths:\n", + " print(\" \", path.name)\n", + "\n", + "if pepsi_blue_path.exists():\n", + " print(\"Blue PEPSI file available but not used in the default red-window validation:\")\n", + " print(\" \", pepsi_blue_path.name)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d871e69f-317b-490c-bbc8-0f9c652d91fc", + "metadata": {}, + "outputs": [], + "source": [ + "def print_table(rows, columns=None, float_formats=None):\n", + " \"\"\"\n", + " Print a compact plain-text table.\n", + "\n", + " This avoids adding a pandas dependency for simple notebook diagnostics.\n", + " \"\"\"\n", + " if rows is None or len(rows) == 0:\n", + " print(\"(no rows)\")\n", + " return\n", + "\n", + " if columns is None:\n", + " columns = list(rows[0].keys())\n", + "\n", + " float_formats = {} if float_formats is None else dict(float_formats)\n", + "\n", + " def format_value(col, value):\n", + " if isinstance(value, (float, np.floating)):\n", + " fmt = float_formats.get(col, None)\n", + " return format(value, fmt) if fmt is not None else str(float(value))\n", + " return str(value)\n", + "\n", + " str_rows = [\n", + " [format_value(col, row.get(col, \"\")) for col in columns]\n", + " for row in rows\n", + " ]\n", + "\n", + " widths = [\n", + " max(len(str(col)), max(len(r[j]) for r in str_rows))\n", + " for j, col in enumerate(columns)\n", + " ]\n", + "\n", + " header = \" | \".join(str(col).ljust(widths[j]) for j, col in enumerate(columns))\n", + " sep = \"-+-\".join(\"-\" * widths[j] for j in range(len(columns)))\n", + "\n", + " print(header)\n", + " print(sep)\n", + " for r in str_rows:\n", + " print(\" | \".join(r[j].ljust(widths[j]) for j in range(len(columns))))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5eb2ec2c-b319-4cb1-9a66-042b78ff20c1", + "metadata": {}, + "outputs": [], + "source": [ + "def concat_with_gap(arrays, gap_value=np.nan, dtype=float):\n", + " \"\"\"\n", + " Concatenate arrays with one separator element between segments.\n", + "\n", + " The separator creates visible gaps in plots, so separate PEPSI line windows\n", + " are not connected by artificial lines.\n", + " \"\"\"\n", + " arrays = [np.asarray(a, dtype=dtype) for a in arrays]\n", + " if len(arrays) == 0:\n", + " return np.array([], dtype=dtype)\n", + "\n", + " out = []\n", + " for i, arr in enumerate(arrays):\n", + " out.append(arr)\n", + " if i < len(arrays) - 1:\n", + " out.append(np.array([gap_value], dtype=dtype))\n", + "\n", + " return np.concatenate(out)\n", + "\n", + "\n", + "def concat_bool_with_gap(arrays):\n", + " \"\"\"Concatenate boolean masks with a False separator between segments.\"\"\"\n", + " return concat_with_gap(arrays, gap_value=False, dtype=bool)" + ] + }, + { + "cell_type": "markdown", + "id": "20f7f2fc-7063-4279-8eaa-98ed2542ea40", + "metadata": {}, + "source": [ + "## Read the PEPSI spectra\n", + "\n", + "We read the two red PEPSI `.dxt.nor` files into `SpectrumSegment` objects.\n", + "\n", + "The PEPSI reader stores useful metadata in each segment, including the object name, wavelength convention, wavelength frame, resolving power, and the `SSBVEL` keyword when available. We keep the original files as full-arm segments first, then build the smaller line-window segments in the next step." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "88ec20c5-fb06-44c6-b279-98461c9cfc4b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name | object | n_pix | wave_min_A | wave_max_A | wave_medium | wave_frame | resolution_R | ssbvel_mps\n", + "----------------------------+-----------+-------+------------+------------+-------------+------------+--------------+-----------\n", + "pepsir.20230603.009.dxt.nor | Gaia21dnc | 17382 | 6235.6 | 7432.4 | unknown | unknown | 50000.0 | -23246.5 \n", + "pepsir.20230603.010.dxt.nor | Gaia21dnc | 21333 | 7356.2 | 9069.6 | unknown | unknown | 50000.0 | -23165.1 \n" + ] + } + ], + "source": [ + "# Read the two red PEPSI spectra.\n", + "\n", + "raw_segments = []\n", + "\n", + "for path in pepsi_paths:\n", + " seg = read_spectrum(path, instrument=\"pepsi\")\n", + "\n", + " # Preserve the source filename in metadata so later window segments can\n", + " # report which PEPSI file they came from.\n", + " meta = dict(seg.meta)\n", + " meta[\"source_file\"] = str(path)\n", + " raw_segments.append(seg.copy(meta=meta))\n", + "\n", + "summary_rows = []\n", + "for seg in raw_segments:\n", + " summary_rows.append(\n", + " {\n", + " \"name\": Path(seg.meta.get(\"source_file\", seg.name)).name,\n", + " \"object\": seg.meta.get(\"object\"),\n", + " \"n_pix\": len(seg.wave),\n", + " \"wave_min_A\": float(np.nanmin(seg.wave)),\n", + " \"wave_max_A\": float(np.nanmax(seg.wave)),\n", + " \"wave_medium\": seg.wave_medium,\n", + " \"wave_frame\": seg.wave_frame,\n", + " \"resolution_R\": seg.meta.get(\"resolution_R\"),\n", + " \"ssbvel_mps\": seg.meta.get(\"ssbvel_mps\"),\n", + " }\n", + " )\n", + "\n", + "print_table(\n", + " summary_rows,\n", + " columns=[\n", + " \"name\",\n", + " \"object\",\n", + " \"n_pix\",\n", + " \"wave_min_A\",\n", + " \"wave_max_A\",\n", + " \"wave_medium\",\n", + " \"wave_frame\",\n", + " \"resolution_R\",\n", + " \"ssbvel_mps\",\n", + " ],\n", + " float_formats={\n", + " \"wave_min_A\": \".1f\",\n", + " \"wave_max_A\": \".1f\",\n", + " \"resolution_R\": \".1f\",\n", + " \"ssbvel_mps\": \".1f\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3cfaf260-56dd-4fbf-ba6f-296f8d235c9f", + "metadata": {}, + "source": [ + "## Build PEPSI legacy line-window segments\n", + "\n", + "The stored PEPSI wavelength convention is marked as unknown by the reader. In the PEPSI red-window tests, the coherent solution was obtained by treating the stored wavelengths as air wavelengths. We therefore set `wave_hypothesis = \"air\"` for this validation." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6a5215e4-37ea-4b45-ae27-2ce32478f19f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Legacy windows defined in air:\n", + " legacy_6495.0 (6485.0, 6505.0)\n", + " legacy_6545.0 (6535.0, 6555.0)\n", + " legacy_6561.0 (6551.0, 6571.0)\n", + " legacy_8498.0 (8488.0, 8508.0)\n", + " legacy_8542.0 (8532.0, 8552.0)\n", + " legacy_8662.0 (8652.0, 8672.0)\n", + "\n", + "Fitted window segments:\n", + " legacy_6495.0 | source: pepsir.20230603.009.dxt.nor | N fit pixels: 288 | working medium: air | working window: (6485.0, 6505.0)\n", + " legacy_6545.0 | source: pepsir.20230603.009.dxt.nor | N fit pixels: 262 | working medium: air | working window: (6535.0, 6555.0)\n", + " legacy_6561.0 | source: pepsir.20230603.009.dxt.nor | N fit pixels: 277 | working medium: air | working window: (6551.0, 6571.0)\n", + " legacy_8498.0 | source: pepsir.20230603.010.dxt.nor | N fit pixels: 202 | working medium: air | working window: (8488.0, 8508.0)\n", + " legacy_8542.0 | source: pepsir.20230603.010.dxt.nor | N fit pixels: 218 | working medium: air | working window: (8532.0, 8552.0)\n", + " legacy_8662.0 | source: pepsir.20230603.010.dxt.nor | N fit pixels: 199 | working medium: air | working window: (8652.0, 8672.0)\n" + ] + } + ], + "source": [ + "# PEPSI legacy-window setup.\n", + "\n", + "wave_hypothesis = \"air\"\n", + "\n", + "legacy_centers_air = None # None uses the recipe default: 6495, 6545, 6561, 8498, 8542, 8662 Å\n", + "legacy_halfwidth_A = 10.0\n", + "legacy_flux_min = 0.2\n", + "legacy_flux_max = 1.1\n", + "window_pad_A = 2.0\n", + "\n", + "input_segments, fit_segments, window_defs_air = build_pepsi_legacy_segments(\n", + " raw_segments,\n", + " wave_hypothesis=wave_hypothesis,\n", + " centers_air=legacy_centers_air,\n", + " halfwidth_A=legacy_halfwidth_A,\n", + " flux_min=legacy_flux_min,\n", + " flux_max=legacy_flux_max,\n", + " window_pad_A=window_pad_A,\n", + ")\n", + "\n", + "print(\"Legacy windows defined in air:\")\n", + "for label, wmin, wmax in window_defs_air:\n", + " print(\" \", label, (wmin, wmax))\n", + "\n", + "print(\"\\nFitted window segments:\")\n", + "for seg in fit_segments:\n", + " working = seg.meta.get(\"legacy_window_working\", None)\n", + " medium = seg.meta.get(\"legacy_window_medium\", seg.wave_medium)\n", + " print(\n", + " \" \",\n", + " seg.name,\n", + " \"| source:\",\n", + " Path(seg.meta.get(\"source_file\", \"\")).name,\n", + " \"| N fit pixels:\",\n", + " int(np.sum(seg.mask)),\n", + " \"| working medium:\",\n", + " medium,\n", + " \"| working window:\",\n", + " None if working is None else working[1:],\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "5edc2467-5281-4451-8dd1-6b4722ca2d2d", + "metadata": {}, + "source": [ + "## Apply the telluric mask and build the SpectrumCollection\n", + "\n", + "The PEPSI red-window smoke test uses Spyctres' built-in telluric mask. The mask function can return float-like values, so we explicitly convert it to a boolean mask before using it for indexing.\n", + "\n", + "After masking, the fitted windows are wrapped in a `SpectrumCollection` with unit weights." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ac51f475-feee-465f-ad62-841f90622065", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collection: pepsi_legacy_windows\n", + "Number of segments: 6\n", + "segment | source | n_fit | weight | wave_min_A | wave_max_A\n", + "--------------+-----------------------------+-------+--------+------------+-----------\n", + "legacy_6495.0 | pepsir.20230603.009.dxt.nor | 283 | 1.00 | 6483.0 | 6507.0 \n", + "legacy_6545.0 | pepsir.20230603.009.dxt.nor | 259 | 1.00 | 6533.1 | 6557.0 \n", + "legacy_6561.0 | pepsir.20230603.009.dxt.nor | 276 | 1.00 | 6549.1 | 6572.9 \n", + "legacy_8498.0 | pepsir.20230603.010.dxt.nor | 202 | 1.00 | 8486.1 | 8510.0 \n", + "legacy_8542.0 | pepsir.20230603.010.dxt.nor | 218 | 1.00 | 8530.0 | 8554.0 \n", + "legacy_8662.0 | pepsir.20230603.010.dxt.nor | 199 | 1.00 | 8650.0 | 8674.0 \n" + ] + } + ], + "source": [ + "# Apply telluric masking to the fitted PEPSI line-window segments.\n", + "\n", + "telluric_threshold = 0.90\n", + "_, telluric_mask = Spyctres.load_telluric_lines(telluric_threshold)\n", + "\n", + "def pepsi_telluric_bad_mask(wave):\n", + " \"\"\"Return True for wavelengths excluded by the telluric mask.\"\"\"\n", + " return np.asarray(telluric_mask(wave)) > 0.5\n", + "\n", + "fit_segments_masked = [\n", + " seg.copy(\n", + " mask=np.asarray(seg.mask, dtype=bool) & ~pepsi_telluric_bad_mask(seg.wave)\n", + " )\n", + " for seg in fit_segments\n", + "]\n", + "\n", + "collection = SpectrumCollection(\n", + " segments=fit_segments_masked,\n", + " weights=np.ones(len(fit_segments_masked), dtype=float),\n", + " meta={\n", + " \"instrument\": \"PEPSI\",\n", + " \"mode\": \"legacy_max\",\n", + " \"wave_hypothesis\": wave_hypothesis,\n", + " \"legacy_windows_air\": list(window_defs_air),\n", + " \"telluric_threshold\": telluric_threshold,\n", + " \"legacy_flux_range\": (legacy_flux_min, legacy_flux_max),\n", + " },\n", + " name=\"pepsi_legacy_windows\",\n", + ")\n", + "\n", + "collection_rows = []\n", + "for seg, weight in zip(collection.segments, collection.weights):\n", + " collection_rows.append(\n", + " {\n", + " \"segment\": seg.name,\n", + " \"source\": Path(seg.meta.get(\"source_file\", \"\")).name,\n", + " \"n_fit\": int(np.sum(seg.mask)),\n", + " \"weight\": float(weight),\n", + " \"wave_min_A\": float(np.nanmin(seg.wave)),\n", + " \"wave_max_A\": float(np.nanmax(seg.wave)),\n", + " }\n", + " )\n", + "\n", + "print(\"Collection:\", collection.name)\n", + "print(\"Number of segments:\", len(collection))\n", + "print_table(\n", + " collection_rows,\n", + " columns=[\"segment\", \"source\", \"n_fit\", \"weight\", \"wave_min_A\", \"wave_max_A\"],\n", + " float_formats={\n", + " \"weight\": \".2f\",\n", + " \"wave_min_A\": \".1f\",\n", + " \"wave_max_A\": \".1f\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8762573e-f6da-466c-b0bd-17813dcab71b", + "metadata": {}, + "source": [ + "## Inspect the selected PEPSI windows\n", + "\n", + "Before fitting, we plot the six selected line-window segments. The shaded region shows the pixels used in the fit after flux filtering and telluric masking. The plotted wavelength range includes the padded support region, while the mask defines the actual comparison region." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "753754f9-1201-4355-85cc-d12804c1d8f2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the PEPSI legacy line-window segments before fitting.\n", + "\n", + "nseg = len(collection)\n", + "fig, axes = plt.subplots(nseg, 1, figsize=(12, 2.4 * nseg), sharex=False)\n", + "\n", + "if nseg == 1:\n", + " axes = [axes]\n", + "\n", + "for ax, seg in zip(axes, collection.segments):\n", + " ax.plot(seg.wave, seg.flux, lw=0.8, label=\"PEPSI data\")\n", + "\n", + " fit_mask = np.asarray(seg.mask, dtype=bool)\n", + " if np.any(fit_mask):\n", + " ax.axvspan(\n", + " float(np.nanmin(seg.wave[fit_mask])),\n", + " float(np.nanmax(seg.wave[fit_mask])),\n", + " alpha=0.15,\n", + " label=\"used pixels\",\n", + " )\n", + "\n", + " ax.set_title(\n", + " f\"{seg.name} | {Path(seg.meta.get('source_file', '')).name}\"\n", + " )\n", + " ax.set_ylabel(\"Normalized flux\")\n", + " ax.legend(loc=\"best\")\n", + "\n", + "axes[-1].set_xlabel(\"Wavelength [Å]\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "dff353e0-2a15-4506-82c4-c4031f002bea", + "metadata": {}, + "source": [ + "## Prepare the PHOENIX model grid and cache\n", + "\n", + "The next step builds the PHOENIX interpolation grid needed by the PEPSI line-window collection.\n", + "\n", + "For speed, this validation uses the same restricted “fast” grid as the PEPSI smoke-test preset. This is enough to test the workflow and should recover the same stable solution basin as the script.\n", + "\n", + "The cache grid is built from stable support windows rather than the final masked pixels. This avoids unnecessary cache rebuilds when the telluric mask or flux mask changes." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bfeb822f-e012-48d7-acd3-4d0286135f35", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Teff grid: [5000. 5100. 5200. 5300. 5400. 5500. 5600. 5700. 5800. 5900. 6000.]\n", + "[Fe/H] grid: [-1.5 -1. -0.5 -0. 0.5]\n", + "logg grid: [2.5 3. 3.5 4. ]\n" + ] + } + ], + "source": [ + "# Set up the PHOENIX library and the reduced validation grid.\n", + "\n", + "phoenix_lib = PhoenixLibrary(phoenix_dir, verbose=True)\n", + "\n", + "teff_avail, feh_avail, logg_avail = phoenix_lib.available_axes()\n", + "\n", + "# Same reduced grid used by the pepsi_legacy_red_fast smoke-test preset.\n", + "teff_grid_req = pick_grid_range(teff_avail, 5000.0, 6000.0)\n", + "feh_grid_req = pick_grid_range(feh_avail, -1.5, 0.5)\n", + "logg_grid_req = pick_grid_range(logg_avail, 2.5, 4.0)\n", + "\n", + "teff_grid_fit, feh_grid_fit, logg_grid_fit = phoenix_lib.complete_subgrid(\n", + " teff_grid_req,\n", + " feh_grid_req,\n", + " logg_grid_req,\n", + ")\n", + "\n", + "print(\"Teff grid:\", teff_grid_fit)\n", + "print(\"[Fe/H] grid:\", feh_grid_fit)\n", + "print(\"logg grid:\", logg_grid_fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c9a1ed59-7fa7-4cb6-b2c6-87afaab21485", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded PHOENIX cache from /tmp/spyctres_pepsi_legacy_linefit_validation_air_fast_cache.npz\n", + "Model wave medium: air\n", + "Model grid size: 223155\n", + "Model wavelength range: 6463.023844076922 8693.962027611222\n", + "Cache path: /tmp/spyctres_pepsi_legacy_linefit_validation_air_fast_cache.npz\n" + ] + } + ], + "source": [ + "# Build or load the PHOENIX cache for the PEPSI legacy support windows.\n", + "\n", + "cache_support_segments = make_pepsi_legacy_cache_support_segments(\n", + " input_segments=input_segments,\n", + " window_defs_air=window_defs_air,\n", + " window_pad_A=window_pad_A,\n", + ")\n", + "\n", + "cache_path = \"/tmp/spyctres_pepsi_legacy_linefit_validation_air_fast_cache.npz\"\n", + "\n", + "model_wave_grid, model_wave_medium = ensure_phoenix_native_interpolator_for_segments(\n", + " segments=cache_support_segments,\n", + " phoenix_lib=phoenix_lib,\n", + " teff_grid=teff_grid_fit,\n", + " feh_grid=feh_grid_fit,\n", + " logg_grid=logg_grid_fit,\n", + " cache_path=cache_path,\n", + " model_margin_A=20.0,\n", + ")\n", + "\n", + "print(\"Model wave medium:\", model_wave_medium)\n", + "print(\"Model grid size:\", len(model_wave_grid))\n", + "print(\"Model wavelength range:\", float(model_wave_grid[0]), float(model_wave_grid[-1]))\n", + "print(\"Cache path:\", cache_path)" + ] + }, + { + "cell_type": "markdown", + "id": "63687264-2869-4f1d-a1a8-6fd38b4ab86a", + "metadata": {}, + "source": [ + "## Define the PEPSI legacy objective\n", + "\n", + "The fit uses one shared parameter vector:\n", + "\n", + "`Teff, [Fe/H], logg, RV, log10(error scale)`\n", + "\n", + "For each trial parameter set, the notebook evaluates the PHOENIX model on all six PEPSI line-window segments, normalizes each local model window with the PEPSI legacy `model / max(model)` comparison, and sums the likelihood over the `SpectrumCollection`.\n", + "\n", + "This objective is kept visible in the notebook so the validation logic is transparent, while the reusable model-evaluation and likelihood terms come from `Spyctres.recipes`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "88a3954c-e5b7-49f5-9c83-d49dc1e503c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Barycentric correction from SSBVEL [km/s]: -23.205791\n", + "Resolving power R: 50000.0\n" + ] + } + ], + "source": [ + "# Shared PEPSI validation settings.\n", + "\n", + "rv_bary_values = []\n", + "for seg in input_segments:\n", + " ssbvel_mps = seg.meta.get(\"ssbvel_mps\")\n", + " if ssbvel_mps is not None:\n", + " rv_bary_values.append(1.0e-3 * float(ssbvel_mps))\n", + "\n", + "rv_bary_kms = float(np.nanmedian(rv_bary_values)) if rv_bary_values else 0.0\n", + "\n", + "R_values = [\n", + " float(seg.meta[\"resolution_R\"])\n", + " for seg in input_segments\n", + " if seg.meta.get(\"resolution_R\") is not None\n", + "]\n", + "R = float(np.nanmedian(R_values)) if len(R_values) else None\n", + "\n", + "model_margin_A = 20.0\n", + "\n", + "print(\"Barycentric correction from SSBVEL [km/s]:\", rv_bary_kms)\n", + "print(\"Resolving power R:\", R)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "853fc165-16ae-457b-bf01-6f3caaf1c0c2", + "metadata": {}, + "outputs": [], + "source": [ + "def pepsi_legacy_objective(x):\n", + " \"\"\"\n", + " Negative log-likelihood for the PEPSI legacy line-window validation.\n", + "\n", + " Parameters\n", + " ----------\n", + " x : sequence\n", + " (Teff, [Fe/H], logg, RV, log10_error_scale)\n", + "\n", + " Returns\n", + " -------\n", + " float\n", + " Summed weighted negative log-likelihood over the SpectrumCollection.\n", + " \"\"\"\n", + " teff, feh, logg, rv, log_scale = map(float, x)\n", + "\n", + " try:\n", + " models = evaluate_pepsi_legacy_max_models(\n", + " phoenix_lib=phoenix_lib,\n", + " segments=collection.segments,\n", + " model_wave_grid=model_wave_grid,\n", + " model_wave_medium=model_wave_medium,\n", + " teff=teff,\n", + " feh=feh,\n", + " logg=logg,\n", + " rv_kms=rv,\n", + " rv_bary_kms=rv_bary_kms,\n", + " R=R,\n", + " model_margin_A=model_margin_A,\n", + " )\n", + " except Exception:\n", + " return 1.0e100\n", + "\n", + " total = 0.0\n", + " n_total = 0\n", + "\n", + " for seg, weight, model in zip(collection.segments, collection.weights, models):\n", + " val, n, _model_norm, _used = pepsi_legacy_max_likelihood_terms(\n", + " seg,\n", + " model,\n", + " log_err_scale=log_scale,\n", + " )\n", + "\n", + " if not np.isfinite(val):\n", + " return 1.0e100\n", + "\n", + " total += float(weight) * float(val)\n", + " n_total += int(n)\n", + "\n", + " if n_total == 0:\n", + " return 1.0e100\n", + "\n", + " return total" + ] + }, + { + "cell_type": "markdown", + "id": "465a15b1-0da2-4720-9eea-2c1c741cef8b", + "metadata": {}, + "source": [ + "## Run the fast validation fit\n", + "\n", + "We now run the same fast validation problem used by the PEPSI smoke-test preset.\n", + "\n", + "The optimizer is seeded with a coarse RV scan. This avoids convergence to a poor local minimum in RV space. The bounds are intentionally restricted for this validation run, so the notebook remains fast and reproducible." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ffbb3a82-6231-4040-870c-4a8185a3e289", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial guess: [ 5.5e+03 -1.0e+00 3.0e+00 -5.0e+01 0.0e+00]\n", + "Lower bounds : [ 5.00e+03 -1.50e+00 2.50e+00 -1.25e+02 -2.00e+00]\n", + "Upper bounds : [6.0e+03 5.0e-01 4.0e+00 2.5e+01 1.0e+00]\n" + ] + } + ], + "source": [ + "# Fast validation bounds and initial guess.\n", + "\n", + "lo = np.array(\n", + " [\n", + " np.min(teff_grid_fit),\n", + " np.min(feh_grid_fit),\n", + " np.min(logg_grid_fit),\n", + " -125.0,\n", + " -2.0,\n", + " ],\n", + " dtype=float,\n", + ")\n", + "\n", + "hi = np.array(\n", + " [\n", + " np.max(teff_grid_fit),\n", + " np.max(feh_grid_fit),\n", + " np.max(logg_grid_fit),\n", + " 25.0,\n", + " 1.0,\n", + " ],\n", + " dtype=float,\n", + ")\n", + "\n", + "x0 = np.array(\n", + " [\n", + " 5500.0, # Teff\n", + " -1.0, # [Fe/H]\n", + " 3.0, # logg\n", + " -50.0, # RV [km/s]\n", + " 0.0, # log10 error scale\n", + " ],\n", + " dtype=float,\n", + ")\n", + "\n", + "x0 = np.minimum(np.maximum(x0, lo), hi)\n", + "\n", + "print(\"Initial guess:\", x0)\n", + "print(\"Lower bounds :\", lo)\n", + "print(\"Upper bounds :\", hi)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a77b0726-c755-47ea-b235-c33c51cefc1d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best coarse RV seed: -50.0\n", + "rv_kms | nll \n", + "-------+----------\n", + "-125.0 | 299170.94\n", + "-106.2 | 255022.41\n", + "-87.5 | 209067.50\n", + "-68.8 | 138995.96\n", + "-50.0 | 43625.00 \n", + "-31.2 | 141226.01\n", + "-12.5 | 212849.89\n", + "6.2 | 244333.67\n", + "25.0 | 287539.95\n" + ] + } + ], + "source": [ + "# Coarse RV seed scan at the initial atmospheric parameters.\n", + "\n", + "rv_grid = np.linspace(lo[3], hi[3], 41)\n", + "rv_scan_rows = []\n", + "\n", + "for rv in rv_grid:\n", + " xt = x0.copy()\n", + " xt[3] = rv\n", + " val = pepsi_legacy_objective(xt)\n", + " rv_scan_rows.append({\"rv_kms\": float(rv), \"nll\": float(val)})\n", + "\n", + "best_rv_row = min(rv_scan_rows, key=lambda row: row[\"nll\"])\n", + "x0[3] = best_rv_row[\"rv_kms\"]\n", + "\n", + "print(\"Best coarse RV seed:\", x0[3])\n", + "print_table(\n", + " rv_scan_rows[::5],\n", + " columns=[\"rv_kms\", \"nll\"],\n", + " float_formats={\"rv_kms\": \".1f\", \"nll\": \".2f\"},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "89906aeb-cc17-428d-bbf7-83ec4912e234", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization success: True\n", + "Message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n", + "Best-fit parameters:\n", + " Teff = 5333.285857735095\n", + " [Fe/H] = -0.9203130341272878\n", + " logg = 2.6408618910326394\n", + " RV = -49.04829979212609\n", + " log10 error scale = 0.7641990129436873\n", + " error scale = 5.8103061045498645\n", + " nll = -2942.3086878737317\n" + ] + } + ], + "source": [ + "# Run the bounded optimizer from the RV-seeded starting point.\n", + "\n", + "fit_result = minimize(\n", + " pepsi_legacy_objective,\n", + " x0,\n", + " method=\"L-BFGS-B\",\n", + " bounds=list(zip(lo, hi)),\n", + " options={\n", + " \"maxiter\": 50,\n", + " \"ftol\": 1e-8,\n", + " },\n", + ")\n", + "\n", + "teff_best, feh_best, logg_best, rv_best, log_scale_best = map(float, fit_result.x)\n", + "\n", + "print(\"Optimization success:\", bool(fit_result.success))\n", + "print(\"Message:\", fit_result.message)\n", + "print(\"Best-fit parameters:\")\n", + "print(\" Teff =\", teff_best)\n", + "print(\" [Fe/H] =\", feh_best)\n", + "print(\" logg =\", logg_best)\n", + "print(\" RV =\", rv_best)\n", + "print(\" log10 error scale =\", log_scale_best)\n", + "print(\" error scale =\", 10.0 ** log_scale_best)\n", + "print(\" nll =\", float(fit_result.fun))" + ] + }, + { + "cell_type": "markdown", + "id": "11dfa40d-6166-4cfb-9dca-951f326383cf", + "metadata": {}, + "source": [ + "## Reconstruct the best-fit line-window models\n", + "\n", + "We now evaluate the best-fit PHOENIX model on each PEPSI line-window segment, apply the same local `model / max(model)` normalization, and compute a final chi-square summary.\n", + "\n", + "The reduced chi-square is computed after applying the fitted error-scale factor. This is useful as a consistency diagnostic, but the fitted error scale should also be reported because it absorbs unmodelled residual structure and underestimated formal errors." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "7005173f-53b2-44f9-93f0-ed3b53d97df3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Global fit summary\n", + "------------------\n", + "Teff = 5333.285857735095\n", + "[Fe/H] = -0.9203130341272878\n", + "logg = 2.6408618910326394\n", + "RV = -49.04829979212609\n", + "log10 error scale = 0.7641990129436873\n", + "error scale = 5.8103061045498645\n", + "chi2 = 1436.999045428659\n", + "dof = 1432\n", + "chi2_red = 1.0034909535116334\n", + "\n", + "Per-segment residual summary\n", + "segment | source | n_fit | weight | chi2 | chi2_per_pix\n", + "--------------+-----------------------------+-------+--------+--------+-------------\n", + "legacy_6495.0 | pepsir.20230603.009.dxt.nor | 283 | 1.00 | 247.92 | 0.876 \n", + "legacy_6545.0 | pepsir.20230603.009.dxt.nor | 259 | 1.00 | 235.45 | 0.909 \n", + "legacy_6561.0 | pepsir.20230603.009.dxt.nor | 276 | 1.00 | 381.30 | 1.382 \n", + "legacy_8498.0 | pepsir.20230603.010.dxt.nor | 202 | 1.00 | 85.99 | 0.426 \n", + "legacy_8542.0 | pepsir.20230603.010.dxt.nor | 218 | 1.00 | 219.36 | 1.006 \n", + "legacy_8662.0 | pepsir.20230603.010.dxt.nor | 199 | 1.00 | 266.98 | 1.342 \n" + ] + } + ], + "source": [ + "# Reconstruct best-fit models and compute final residual statistics.\n", + "\n", + "best_models_raw = evaluate_pepsi_legacy_max_models(\n", + " phoenix_lib=phoenix_lib,\n", + " segments=collection.segments,\n", + " model_wave_grid=model_wave_grid,\n", + " model_wave_medium=model_wave_medium,\n", + " teff=teff_best,\n", + " feh=feh_best,\n", + " logg=logg_best,\n", + " rv_kms=rv_best,\n", + " rv_bary_kms=rv_bary_kms,\n", + " R=R,\n", + " model_margin_A=model_margin_A,\n", + ")\n", + "\n", + "model_norm_list = []\n", + "used_masks = []\n", + "segment_rows = []\n", + "\n", + "chi2 = 0.0\n", + "npts = 0\n", + "\n", + "for seg, weight, model in zip(collection.segments, collection.weights, best_models_raw):\n", + " nll_i, n_i, model_norm_i, used_i = pepsi_legacy_max_likelihood_terms(\n", + " seg,\n", + " model,\n", + " log_err_scale=log_scale_best,\n", + " )\n", + "\n", + " err_scaled = np.asarray(seg.err, dtype=float)[used_i] * (10.0 ** log_scale_best)\n", + " resid_scaled = (\n", + " np.asarray(seg.flux, dtype=float)[used_i] - model_norm_i[used_i]\n", + " ) / err_scaled\n", + "\n", + " chi2_i = float(np.sum(resid_scaled * resid_scaled))\n", + " chi2 += float(weight) * chi2_i\n", + " npts += int(n_i)\n", + "\n", + " model_norm_list.append(model_norm_i)\n", + " used_masks.append(used_i)\n", + "\n", + " segment_rows.append(\n", + " {\n", + " \"segment\": seg.name,\n", + " \"source\": Path(seg.meta.get(\"source_file\", \"\")).name,\n", + " \"n_fit\": int(n_i),\n", + " \"weight\": float(weight),\n", + " \"chi2\": chi2_i,\n", + " \"chi2_per_pix\": chi2_i / max(1, int(n_i)),\n", + " }\n", + " )\n", + "\n", + "dof = max(1, npts - 5)\n", + "chi2_red = chi2 / dof\n", + "\n", + "print(\"Global fit summary\")\n", + "print(\"------------------\")\n", + "print(\"Teff =\", teff_best)\n", + "print(\"[Fe/H] =\", feh_best)\n", + "print(\"logg =\", logg_best)\n", + "print(\"RV =\", rv_best)\n", + "print(\"log10 error scale =\", log_scale_best)\n", + "print(\"error scale =\", 10.0 ** log_scale_best)\n", + "print(\"chi2 =\", chi2)\n", + "print(\"dof =\", dof)\n", + "print(\"chi2_red =\", chi2_red)\n", + "\n", + "print(\"\\nPer-segment residual summary\")\n", + "print_table(\n", + " segment_rows,\n", + " columns=[\"segment\", \"source\", \"n_fit\", \"weight\", \"chi2\", \"chi2_per_pix\"],\n", + " float_formats={\n", + " \"weight\": \".2f\",\n", + " \"chi2\": \".2f\",\n", + " \"chi2_per_pix\": \".3f\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ca100356-cc68-4260-aa3c-1000437700c7", + "metadata": {}, + "source": [ + "## Paper-style line-window comparison plot\n", + "\n", + "The compact full-spectrum diagnostic plot is not ideal for this validation because the PEPSI windows are widely separated in wavelength. For inspection, it is clearer to plot each fitted line window in its own panel, similar to the PEPSI figure in the paper.\n", + "\n", + "The plot below shows the normalized PEPSI data and the locally normalized PHOENIX model in each selected window." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9effcb2b-31a6-4146-90a3-9a460c94dcb9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Paper-style multi-panel plot for the PEPSI legacy windows.\n", + "\n", + "nseg = len(collection.segments)\n", + "\n", + "fig, axes = plt.subplots(\n", + " 1,\n", + " nseg,\n", + " figsize=(2.5 * nseg, 3.4),\n", + " sharey=True,\n", + ")\n", + "\n", + "if nseg == 1:\n", + " axes = [axes]\n", + "\n", + "for i, (ax, seg, model_norm, used) in enumerate(\n", + " zip(axes, collection.segments, model_norm_list, used_masks)\n", + "):\n", + " wave = np.asarray(seg.wave, dtype=float)\n", + " flux = np.asarray(seg.flux, dtype=float)\n", + " err_scaled = np.asarray(seg.err, dtype=float) * (10.0 ** log_scale_best)\n", + " used = np.asarray(used, dtype=bool)\n", + "\n", + " # Plot only the pixels used in the likelihood.\n", + " w = wave[used]\n", + " f = flux[used]\n", + " e = err_scaled[used]\n", + " m = np.asarray(model_norm, dtype=float)[used]\n", + "\n", + " order = np.argsort(w)\n", + " w = w[order]\n", + " f = f[order]\n", + " e = e[order]\n", + " m = m[order]\n", + "\n", + " ax.errorbar(\n", + " w,\n", + " f,\n", + " yerr=e,\n", + " fmt=\".\",\n", + " ms=2.3,\n", + " lw=0.45,\n", + " alpha=0.75,\n", + " label=\"Data\" if i == 0 else None,\n", + " )\n", + " ax.plot(\n", + " w,\n", + " m,\n", + " lw=1.4,\n", + " label=\"Model\" if i == 0 else None,\n", + " )\n", + "\n", + " ax.set_xlim(float(np.nanmin(w)), float(np.nanmax(w)))\n", + " ax.set_ylim(0.2, 1.15)\n", + "\n", + " label = seg.name.replace(\"legacy_\", \"\")\n", + " ax.set_title(label, fontsize=10)\n", + " ax.set_xlabel(r\"$\\lambda$ [$\\AA$]\")\n", + " ax.tick_params(axis=\"both\", labelsize=8)\n", + "\n", + " if i == 0:\n", + " ax.set_ylabel(\"Normalized flux\")\n", + " ax.legend(loc=\"lower left\", fontsize=8, frameon=True)\n", + "\n", + "caption = (\n", + " \"PEPSI legacy line-window validation: \"\n", + " f\"Teff={teff_best:.0f} K, [Fe/H]={feh_best:.2f}, \"\n", + " f\"logg={logg_best:.2f}, RV={rv_best:.1f} km/s, \"\n", + " f\"chi2_red={chi2_red:.2f}.\"\n", + ")\n", + "\n", + "fig.text(0.5, -0.03, caption, ha=\"center\", va=\"top\", fontsize=10)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5c1a1086-a539-433a-a033-2bb661fdac55", + "metadata": {}, + "source": [ + "## Interpretation\n", + "\n", + "This notebook reproduces the same fast PEPSI legacy line-window validation result as the `pepsi_fit_smoketest.py` preset.\n", + "\n", + "The important checks are:\n", + "\n", + "- the red PEPSI spectra are read into `SpectrumSegment` objects;\n", + "- the selected local line windows are converted into a `SpectrumCollection`;\n", + "- telluric masking and flux filtering reproduce the same fitted pixel counts as the smoke test;\n", + "- the PHOENIX model is evaluated through the native-grid forward model with instrumental broadening;\n", + "- the optimizer recovers the same stable RV basin near `-49 km/s`;\n", + "- the final reduced chi-square is close to one after applying the fitted error-scale factor.\n", + "\n", + "This notebook is not intended to be the public classification workflow. The public multi-segment classification example should remain methodologically uniform and use the generic `fit_phoenix_full_spectrum()` path.\n", + "\n", + "Here, the purpose is narrower: to show that the shared Spyctres infrastructure can also support the PEPSI legacy line-window workflow in a modular way. The PEPSI-specific part is the local window normalization and likelihood, while the spectrum containers, PHOENIX backend, wavelength handling, broadening, and cache logic are shared with the rest of the PHOENIX fitting framework.\n", + "\n", + "The fitted parameters should therefore be interpreted as a validation result for this line-window setup, not as a final source-parameter determination." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "81b3cf6b-77bc-4f89-88a8-5cf46b64d770", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PEPSI legacy line-window validation\n", + "========================================\n", + "Collection: pepsi_legacy_windows\n", + "Segments: ['legacy_6495.0', 'legacy_6545.0', 'legacy_6561.0', 'legacy_8498.0', 'legacy_8542.0', 'legacy_8662.0']\n", + "Weights: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n", + "\n", + "Best-fit parameters\n", + "-------------------\n", + "Teff = 5333.3 K\n", + "[Fe/H] = -0.920\n", + "logg = 2.641\n", + "RV = -49.048 km/s\n", + "log10 error scale = 0.764\n", + "error scale = 5.810\n", + "\n", + "Fit quality\n", + "-----------\n", + "nll = -2942.309\n", + "chi2 = 1436.999\n", + "dof = 1432\n", + "chi2_red = 1.0035\n" + ] + } + ], + "source": [ + "print(\"PEPSI legacy line-window validation\")\n", + "print(\"=\" * 40)\n", + "print(\"Collection:\", collection.name)\n", + "print(\"Segments:\", list(collection.names))\n", + "print(\"Weights:\", collection.weights.tolist())\n", + "print()\n", + "print(\"Best-fit parameters\")\n", + "print(\"-------------------\")\n", + "print(f\"Teff = {teff_best:.1f} K\")\n", + "print(f\"[Fe/H] = {feh_best:.3f}\")\n", + "print(f\"logg = {logg_best:.3f}\")\n", + "print(f\"RV = {rv_best:.3f} km/s\")\n", + "print(f\"log10 error scale = {log_scale_best:.3f}\")\n", + "print(f\"error scale = {10.0 ** log_scale_best:.3f}\")\n", + "print()\n", + "print(\"Fit quality\")\n", + "print(\"-----------\")\n", + "print(f\"nll = {float(fit_result.fun):.3f}\")\n", + "print(f\"chi2 = {chi2:.3f}\")\n", + "print(f\"dof = {dof}\")\n", + "print(f\"chi2_red = {chi2_red:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18867406-7140-464b-9a15-397e955183f2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (spyctres2)", + "language": "python", + "name": "spyctres2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/xshooter_multiarm_classification.ipynb b/examples/xshooter_multiarm_classification.ipynb new file mode 100644 index 0000000..e2eb898 --- /dev/null +++ b/examples/xshooter_multiarm_classification.ipynb @@ -0,0 +1,922 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "964ce8e5", + "metadata": {}, + "source": [ + "# X-SHOOTER multi-arm PHOENIX classification\n", + "\n", + "This notebook is an advanced example showing how to fit one PHOENIX model to selected windows from the UVB, VIS, and NIR arms of a single X-SHOOTER spectrum.\n", + "\n", + "The fit shares one stellar parameter set across all segments, while each segment keeps its own local continuum correction.\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3cd4d48", + "metadata": {}, + "source": [ + "## 1. Imports\n", + "\n", + "We import the Spyctres interfaces needed for reading spectra, building a `SpectrumCollection`, fitting the PHOENIX model, and plotting the result.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "462a5594", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\n", + " \"ignore\",\n", + " message=r\"pkg_resources is deprecated as an API.*\",\n", + " category=UserWarning,\n", + " module=r\"pysynphot.*\",\n", + ")\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from Spyctres.config import load_user_config, get_config_value, resolve_setting\n", + "from Spyctres.io import read_spectrum, make_padded_window_segments, SpectrumCollection\n", + "from Spyctres.phoenix import PhoenixLibrary\n", + "from Spyctres.recipes import (\n", + " attach_balmer_metadata,\n", + " normalize_segments_sidebands,\n", + " make_balmer_core_exclude_mask,\n", + ")\n", + "from Spyctres.fitting import (\n", + " fit_phoenix_full_spectrum,\n", + " reconstruct_phoenix_legendre_models_for_segments,\n", + ")\n", + "\n", + "def print_table(rows, columns=None, float_formats=None):\n", + " if rows is None or len(rows) == 0:\n", + " print(\"(no rows)\")\n", + " return\n", + "\n", + " if columns is None:\n", + " columns = list(rows[0].keys())\n", + " float_formats = {} if float_formats is None else dict(float_formats)\n", + "\n", + " def format_value(col, value):\n", + " if isinstance(value, (float, np.floating)):\n", + " fmt = float_formats.get(col, None)\n", + " return format(value, fmt) if fmt is not None else str(float(value))\n", + " return str(value)\n", + "\n", + " str_rows = []\n", + " for row in rows:\n", + " str_rows.append([format_value(col, row.get(col, \"\")) for col in columns])\n", + "\n", + " widths = []\n", + " for j, col in enumerate(columns):\n", + " widths.append(max(len(str(col)), max(len(r[j]) for r in str_rows)))\n", + "\n", + " header = \" | \".join(str(col).ljust(widths[j]) for j, col in enumerate(columns))\n", + " sep = \"-+-\".join(\"-\" * widths[j] for j in range(len(columns)))\n", + "\n", + " print(header)\n", + " print(sep)\n", + " for r in str_rows:\n", + " print(\" | \".join(r[j].ljust(widths[j]) for j in range(len(columns))))\n" + ] + }, + { + "cell_type": "markdown", + "id": "573c30de", + "metadata": {}, + "source": [ + "## 2. Locate the PHOENIX templates and the example data\n", + "\n", + "The PHOENIX path comes from the same config or environment-variable logic used elsewhere in Spyctres.\n", + "\n", + "The notebook can be run either from the repo root or from the `examples/` directory.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a2727918", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PHOENIX dir: /home/Tux/ytsapras/Installed_Programs/astroARIADNE/Models_Dir/PHOENIXv2\n", + "Data dir : /home/Tux/ytsapras/Installed_Programs/Spyctres-dev/examples/data\n", + "Files:\n", + " TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_UVB.fits\n", + " TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_VIS_TELL_CORR.fits\n", + " TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_NIR_TELL_CORR.fits\n" + ] + } + ], + "source": [ + "config = load_user_config()\n", + "phoenix_dir_cfg = get_config_value(config, \"paths\", \"phoenix_dir\", default=None)\n", + "phoenix_dir = resolve_setting(\n", + " None,\n", + " env_var_name=\"SPYCTRES_PHOENIX_DIR\",\n", + " config_value=phoenix_dir_cfg,\n", + " default=None,\n", + ")\n", + "if phoenix_dir is None:\n", + " raise RuntimeError(\n", + " \"No PHOENIX directory found. Set SPYCTRES_PHOENIX_DIR or configure \"\n", + " \"~/.config/spyctres/config.toml.\"\n", + " )\n", + "\n", + "cwd = Path.cwd().resolve()\n", + "if (cwd / \"examples\" / \"data\").exists():\n", + " data_dir = cwd / \"examples\" / \"data\"\n", + "elif (cwd / \"data\").exists():\n", + " data_dir = cwd / \"data\"\n", + "else:\n", + " raise FileNotFoundError(\n", + " \"Could not find the example data directory. Run this notebook from the repo root \"\n", + " \"or from the examples/ directory.\"\n", + " )\n", + "\n", + "uvb_path = data_dir / \"TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_UVB.fits\"\n", + "vis_path = data_dir / \"TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_VIS_TELL_CORR.fits\"\n", + "nir_path = data_dir / \"TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_NIR_TELL_CORR.fits\"\n", + "\n", + "for path in [uvb_path, vis_path, nir_path]:\n", + " if not path.exists():\n", + " raise FileNotFoundError(path)\n", + "\n", + "print(\"PHOENIX dir:\", phoenix_dir)\n", + "print(\"Data dir :\", data_dir)\n", + "print(\"Files:\")\n", + "for path in [uvb_path, vis_path, nir_path]:\n", + " print(\" \", path.name)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b53c5780", + "metadata": {}, + "source": [ + "## 3. Read the three merged X-SHOOTER spectra\n", + "\n", + "The reader returns one `SpectrumSegment` per arm and stores useful metadata such as the wavelength medium, frame, and nominal resolving power.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5c291339", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name | arm | object | n_pix | wave_min_A | wave_max_A | wave_medium | wave_frame | resolution_R | telluric_corrected\n", + "-------------------------------------------------------+-----+-----------+-------+------------+------------+-------------+-------------+--------------+-------------------\n", + "TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_UVB.fits | UVB | Gaia21ccu | 12854 | 2989.2 | 5559.8 | air | topocentric | 5400.0 | False \n", + "TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_VIS_TELL_CORR.fits | VIS | Gaia21ccu | 24318 | 5336.6 | 10200.0 | air | topocentric | 11000.0 | True \n", + "TOO_Gaia21ccu_SCI_SLIT_FLUX_MERGE1D_NIR_TELL_CORR.fits | NIR | Gaia21ccu | 24750 | 9940.2 | 24789.6 | air | topocentric | 8100.0 | True \n" + ] + } + ], + "source": [ + "uvb = read_spectrum(uvb_path, instrument=\"xshooter\")\n", + "vis = read_spectrum(vis_path, instrument=\"xshooter\")\n", + "nir = read_spectrum(nir_path, instrument=\"xshooter\")\n", + "\n", + "arms = [uvb, vis, nir]\n", + "\n", + "summary_rows = []\n", + "for seg in arms:\n", + " summary_rows.append(\n", + " {\n", + " \"name\": seg.name,\n", + " \"arm\": seg.meta.get(\"arm\"),\n", + " \"object\": seg.meta.get(\"object\"),\n", + " \"n_pix\": len(seg.wave),\n", + " \"wave_min_A\": float(np.min(seg.wave)),\n", + " \"wave_max_A\": float(np.max(seg.wave)),\n", + " \"wave_medium\": seg.wave_medium,\n", + " \"wave_frame\": seg.wave_frame,\n", + " \"resolution_R\": seg.meta.get(\"resolution_R\"),\n", + " \"telluric_corrected\": seg.meta.get(\"telluric_corrected\"),\n", + " }\n", + " )\n", + "\n", + "print_table(\n", + " summary_rows,\n", + " columns=[\n", + " \"name\",\n", + " \"arm\",\n", + " \"object\",\n", + " \"n_pix\",\n", + " \"wave_min_A\",\n", + " \"wave_max_A\",\n", + " \"wave_medium\",\n", + " \"wave_frame\",\n", + " \"resolution_R\",\n", + " \"telluric_corrected\",\n", + " ],\n", + " float_formats={\n", + " \"wave_min_A\": \".1f\",\n", + " \"wave_max_A\": \".1f\",\n", + " \"resolution_R\": \".1f\",\n", + " },\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "6b8f688c", + "metadata": {}, + "source": [ + "## 4. Quick look at the three arms\n", + "\n", + "This is only a visual check. For plotting only, we trim away the noisiest edges and set the y-range from robust percentiles so the line structure is easier to see.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2d0bccd8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display_ranges = {\n", + " \"UVB\": (3200.0, 5550.0),\n", + " \"VIS\": (5450.0, 10250.0),\n", + " \"NIR\": (10000.0, 24800.0),\n", + "}\n", + "\n", + "fig, axes = plt.subplots(3, 1, figsize=(12, 9), sharex=False)\n", + "\n", + "for ax, seg, arm in zip(axes, [uvb, vis, nir], [\"UVB\", \"VIS\", \"NIR\"]):\n", + " lo, hi = display_ranges[arm]\n", + "\n", + " m = np.isfinite(seg.wave) & np.isfinite(seg.flux)\n", + " m &= (seg.wave >= lo) & (seg.wave <= hi)\n", + "\n", + " wave = np.asarray(seg.wave[m], dtype=float)\n", + " flux = np.asarray(seg.flux[m], dtype=float)\n", + "\n", + " ax.plot(wave, flux, lw=0.8)\n", + " ax.set_title(f\"{arm} | {seg.name}\")\n", + " ax.set_ylabel(\"Flux\")\n", + "\n", + " if flux.size > 10:\n", + " y1, y2 = np.nanpercentile(flux, [1.0, 99.0])\n", + " pad = 0.1 * (y2 - y1) if y2 > y1 else 1.0\n", + " ax.set_ylim(y1 - pad, y2 + pad)\n", + "\n", + "axes[-1].set_xlabel(\"Wavelength [Å]\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "5c37a981", + "metadata": {}, + "source": [ + "## 5. Define the fit windows\n", + "\n", + "We use three Balmer-line windows in the UVB arm, one hydrogen-line window in the VIS arm, and one optional NIR hydrogen-line window.\n", + "\n", + "These are editable starting points. If your data suggest different boundaries, you can change the wavelength ranges directly in the code cell below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "be9b7f27", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UVB windows: [('Hdelta', 3990.0, 4220.0), ('Hgamma', 4220.0, 4480.0), ('Hbeta', 4700.0, 5020.0)]\n", + "VIS windows: [('Halpha', 6500.0, 6625.0)]\n", + "NIR windows: [('Pabeta', 12775.0, 12855.0)]\n" + ] + } + ], + "source": [ + "uvb_balmer_windows = [\n", + " (\"Hdelta\", 3990.0, 4220.0),\n", + " (\"Hgamma\", 4220.0, 4480.0),\n", + " (\"Hbeta\", 4700.0, 5020.0),\n", + "]\n", + "\n", + "vis_windows = [\n", + " (\"Halpha\", 6500.0, 6625.0),\n", + "]\n", + "\n", + "nir_windows = [\n", + " (\"Pabeta\", 12775.0, 12855.0),\n", + "]\n", + "\n", + "print(\"UVB windows:\", uvb_balmer_windows)\n", + "print(\"VIS windows:\", vis_windows)\n", + "print(\"NIR windows:\", nir_windows)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6c3b612f", + "metadata": {}, + "source": [ + "## 6. Build the per-window segments\n", + "\n", + "Each segment keeps a padded support region, while its mask marks the inner fit window.\n", + "\n", + "For the broad UVB Balmer lines, we also apply a simple local sideband normalization and exclude a narrow line core. The VIS and NIR windows are left for the fitter's local continuum model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1aad4c87", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UVB segment names: ['UVB_Hdelta', 'UVB_Hgamma', 'UVB_Hbeta']\n", + "VIS segment names: ['VIS_Halpha']\n", + "NIR segment names: ['NIR_Pabeta']\n" + ] + } + ], + "source": [ + "# Restrict the UVB arm to the wavelength region used in this example.\n", + "uvb_clip = uvb.window(wmin=3950.0, wmax=5050.0, name_suffix=\"fitwin\")\n", + "\n", + "uvb_segments = make_padded_window_segments(\n", + " uvb_clip,\n", + " uvb_balmer_windows,\n", + " pad=20.0,\n", + " name_prefix=\"uvb_balmer\",\n", + ")\n", + "\n", + "# Give the UVB windows explicit line labels before attaching metadata.\n", + "uvb_recipe_labels = [\"Hdelta\", \"Hgamma\", \"Hbeta\"]\n", + "for seg_i, label in zip(uvb_segments, uvb_recipe_labels):\n", + " seg_i.name = label\n", + "\n", + "attach_balmer_metadata(uvb_segments)\n", + "\n", + "for seg_i, label in zip(uvb_segments, [\"Hdelta\", \"Hgamma\", \"Hbeta\"]):\n", + " seg_i.name = f\"UVB_{label}\"\n", + " \n", + "uvb_segments, uvb_norm_info = normalize_segments_sidebands(\n", + " uvb_segments,\n", + " sideband_width=10.0,\n", + " sideband_order=1,\n", + ")\n", + "\n", + "uvb_exclude_mask = make_balmer_core_exclude_mask(\n", + " core_halfwidth=6.0,\n", + " wave_medium=uvb_segments[0].wave_medium,\n", + ")\n", + "\n", + "# VIS and NIR windows\n", + "vis_segments = make_padded_window_segments(\n", + " vis,\n", + " vis_windows,\n", + " pad=20.0,\n", + " name_prefix=\"vis\",\n", + ")\n", + "for seg_i, (label, _w0, _w1) in zip(vis_segments, vis_windows):\n", + " seg_i.name = f\"VIS_{label}\"\n", + "\n", + "nir_segments = make_padded_window_segments(\n", + " nir,\n", + " nir_windows,\n", + " pad=30.0,\n", + " name_prefix=\"nir\",\n", + ")\n", + "for seg_i, (label, _w0, _w1) in zip(nir_segments, nir_windows):\n", + " seg_i.name = f\"NIR_{label}\"\n", + "\n", + "print(\"UVB segment names:\", [s.name for s in uvb_segments])\n", + "print(\"VIS segment names:\", [s.name for s in vis_segments])\n", + "print(\"NIR segment names:\", [s.name for s in nir_segments])" + ] + }, + { + "cell_type": "markdown", + "id": "9e190428", + "metadata": {}, + "source": [ + "## 7. Inspect the selected windows\n", + "\n", + "This lets us see exactly what will be passed to the fitter.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "61afe5fd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "all_segments_for_plot = uvb_segments + vis_segments + nir_segments\n", + "\n", + "nseg = len(all_segments_for_plot)\n", + "fig, axes = plt.subplots(nseg, 1, figsize=(12, 2.6 * nseg), sharex=False)\n", + "\n", + "if nseg == 1:\n", + " axes = [axes]\n", + "\n", + "for ax, seg in zip(axes, all_segments_for_plot):\n", + " ax.plot(seg.wave, seg.flux, lw=0.8)\n", + " fit_mask = np.asarray(seg.mask, dtype=bool)\n", + " if np.any(fit_mask):\n", + " ax.axvspan(seg.wave[fit_mask].min(), seg.wave[fit_mask].max(), alpha=0.15)\n", + " ax.set_title(seg.name)\n", + " ax.set_ylabel(\"Flux\")\n", + "\n", + "axes[-1].set_xlabel(\"Wavelength [Å]\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "0945aa04", + "metadata": {}, + "source": [ + "## 8. Build the `SpectrumCollection` objects\n", + "\n", + "We compare three cases:\n", + "- UVB only\n", + "- UVB + VIS\n", + "- UVB + VIS + NIR\n", + "\n", + "By default, we balance the total weight of each arm so one arm does not dominate simply because it contributes more windows.\n" + ] + }, + { + "cell_type": "markdown", + "id": "5b96ee0e-295d-4fa1-aacc-9402c2343953", + "metadata": {}, + "source": [ + "This notebook sideband-normalizes the UVB Balmer windows before fitting, while the simpler UVB notebook fits the original flux with a per-window polynomial continuum. The two examples therefore test related but not identical workflows. Agreement at the broad classification level is more important than exact equality of the fitted parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "934ff4ae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UVB only\n", + " n_segments: 3\n", + " weights : [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n", + " names : ['UVB_Hdelta', 'UVB_Hgamma', 'UVB_Hbeta']\n", + "UVB + VIS\n", + " n_segments: 4\n", + " weights : [0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 1.0]\n", + " names : ['UVB_Hdelta', 'UVB_Hgamma', 'UVB_Hbeta', 'VIS_Halpha']\n", + "UVB + VIS + NIR\n", + " n_segments: 5\n", + " weights : [0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 1.0, 1.0]\n", + " names : ['UVB_Hdelta', 'UVB_Hgamma', 'UVB_Hbeta', 'VIS_Halpha', 'NIR_Pabeta']\n" + ] + } + ], + "source": [ + "def make_arm_balanced_weights(*segment_groups):\n", + " weights = []\n", + " for group in segment_groups:\n", + " if len(group) == 0:\n", + " continue\n", + " w = 1.0 / float(len(group))\n", + " weights.extend([w] * len(group))\n", + " return np.asarray(weights, dtype=float)\n", + "\n", + "uvb_only_segments = list(uvb_segments)\n", + "uvb_vis_segments = list(uvb_segments) + list(vis_segments)\n", + "uvb_vis_nir_segments = list(uvb_segments) + list(vis_segments) + list(nir_segments)\n", + "\n", + "# 1. UVB-only is the baseline classification.\n", + "uvb_only = SpectrumCollection(\n", + " uvb_only_segments,\n", + " weights=make_arm_balanced_weights(uvb_segments),\n", + " name=\"UVB only\",\n", + ")\n", + "\n", + "# 2. UVB + Halpha is a consistency check.\n", + "uvb_vis = SpectrumCollection(\n", + " uvb_vis_segments,\n", + " weights=make_arm_balanced_weights(uvb_segments, vis_segments),\n", + " name=\"UVB + VIS\",\n", + ")\n", + "\n", + "# 3. UVB + Halpha + Pabeta is exploratory.\n", + "uvb_vis_nir = SpectrumCollection(\n", + " uvb_vis_nir_segments,\n", + " weights=make_arm_balanced_weights(uvb_segments, vis_segments, nir_segments),\n", + " name=\"UVB + VIS + NIR\",\n", + ")\n", + "\n", + "for coll in [uvb_only, uvb_vis, uvb_vis_nir]:\n", + " print(coll.name)\n", + " print(\" n_segments:\", len(coll))\n", + " print(\" weights :\", coll.weights.tolist())\n", + " print(\" names :\", coll.names)\n" + ] + }, + { + "cell_type": "markdown", + "id": "20d3980d", + "metadata": {}, + "source": [ + "## 9. Inspect the segment-specific broadening metadata\n", + "\n", + "The fitter can now use segment-specific line-spread information from the segment metadata.\n", + "\n", + "Before fitting, it is useful to confirm what each selected segment carries. In this example, the X-SHOOTER reader provides a nominal resolving power `resolution_R` for each arm. We let the fitter use those per-segment values directly, rather than forcing one common value across all windows.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f9964510", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UVB_Hdelta resolution_R = 5400.0 fwhm_kms = None lsf_fwhm_kms = None\n", + "UVB_Hgamma resolution_R = 5400.0 fwhm_kms = None lsf_fwhm_kms = None\n", + "UVB_Hbeta resolution_R = 5400.0 fwhm_kms = None lsf_fwhm_kms = None\n", + "VIS_Halpha resolution_R = 11000.0 fwhm_kms = None lsf_fwhm_kms = None\n", + "NIR_Pabeta resolution_R = 8100.0 fwhm_kms = None lsf_fwhm_kms = None\n" + ] + } + ], + "source": [ + "all_selected_segments = uvb_vis_nir_segments\n", + "\n", + "for seg in all_selected_segments:\n", + " print(\n", + " seg.name,\n", + " \"resolution_R =\", seg.meta.get(\"resolution_R\"),\n", + " \"fwhm_kms =\", seg.meta.get(\"fwhm_kms\"),\n", + " \"lsf_fwhm_kms =\", seg.meta.get(\"lsf_fwhm_kms\"),\n", + " )\n", + "\n", + "phoenix_lib = PhoenixLibrary(phoenix_dir, verbose=False)\n" + ] + }, + { + "cell_type": "markdown", + "id": "06fe5941", + "metadata": {}, + "source": [ + "## 10. Define a small fit helper\n", + "\n", + "To keep the notebook readable, we wrap the main fitter call in a short helper function.\n", + "\n", + "Here we pass only the settings that matter most for this example. In particular, we do **not** force a single global resolving power. The fitter now reads the segment-specific broadening information directly from the segment metadata.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cce3e058", + "metadata": {}, + "outputs": [], + "source": [ + "teff_grid = np.array([9600., 9800., 10000., 10200., 10400., 10600., 10800., 11000., 11200., 11400., 11600., 11800.])\n", + "feh_grid = np.array([-0.5, 0.0, 0.5])\n", + "logg_grid = np.array([3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0])\n", + "\n", + "p0 = (9800.0, -0.5, 3.0, -5.0)\n", + "\n", + "def run_collection_fit(collection, exclude_mask=None, label=None):\n", + " result = fit_phoenix_full_spectrum(\n", + " segments=collection,\n", + " phoenix_lib=phoenix_lib,\n", + " p0=p0,\n", + " exclude_mask=exclude_mask,\n", + " mdeg=1,\n", + " forward_model=\"native_interp\",\n", + " teff_grid=teff_grid,\n", + " feh_grid=feh_grid,\n", + " logg_grid=logg_grid,\n", + " rv_init=\"grid\",\n", + " verbose=0,\n", + " max_nfev=200,\n", + " )\n", + " if label is not None:\n", + " result[\"label\"] = label\n", + " return result\n" + ] + }, + { + "cell_type": "markdown", + "id": "4ade7877", + "metadata": {}, + "source": [ + "## 11. Run the fits\n", + "\n", + "We first fit the UVB-only collection, then add the VIS window, and finally add the NIR windows.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1a959346", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label | teff | feh | logg | rv_kms | chi2_red | n_segments | success\n", + "----------------+---------+--------+-------+--------+----------+------------+--------\n", + "UVB only | 10801.6 | -0.435 | 3.066 | -10.57 | 2.265 | 3 | True \n", + "UVB + VIS | 11779.0 | -0.497 | 3.184 | -3.05 | 2.765 | 4 | True \n", + "UVB + VIS + NIR | 11767.7 | -0.500 | 3.201 | -1.08 | 2.912 | 5 | True \n", + "WARNING: UVB + VIS + NIR is at a metallicity grid edge.\n" + ] + } + ], + "source": [ + "results = []\n", + "\n", + "results.append(run_collection_fit(uvb_only, exclude_mask=uvb_exclude_mask, label=\"UVB only\"))\n", + "results.append(run_collection_fit(uvb_vis, exclude_mask=uvb_exclude_mask, label=\"UVB + VIS\"))\n", + "results.append(run_collection_fit(uvb_vis_nir, exclude_mask=uvb_exclude_mask, label=\"UVB + VIS + NIR\"))\n", + "\n", + "fit_rows = []\n", + "for res in results:\n", + " fit_rows.append(\n", + " {\n", + " \"label\": res[\"label\"],\n", + " \"teff\": res[\"teff\"],\n", + " \"feh\": res[\"feh\"],\n", + " \"logg\": res[\"logg\"],\n", + " \"rv_kms\": res[\"rv_kms\"],\n", + " \"chi2_red\": res[\"chi2_red\"],\n", + " \"n_segments\": res[\"n_segments\"],\n", + " \"success\": res[\"success\"],\n", + " }\n", + " )\n", + "\n", + "print_table(\n", + " fit_rows,\n", + " columns=[\"label\", \"teff\", \"feh\", \"logg\", \"rv_kms\", \"chi2_red\", \"n_segments\", \"success\"],\n", + " float_formats={\n", + " \"teff\": \".1f\",\n", + " \"feh\": \".3f\",\n", + " \"logg\": \".3f\",\n", + " \"rv_kms\": \".2f\",\n", + " \"chi2_red\": \".3f\",\n", + " },\n", + ")\n", + "\n", + "for row in fit_rows:\n", + " if abs(row[\"teff\"] - teff_grid.max()) < 1.0:\n", + " print(f\"WARNING: {row['label']} is at the upper Teff grid edge.\")\n", + " if abs(row[\"feh\"] - feh_grid.min()) < 1e-3 or abs(row[\"feh\"] - feh_grid.max()) < 1e-3:\n", + " print(f\"WARNING: {row['label']} is at a metallicity grid edge.\")" + ] + }, + { + "cell_type": "markdown", + "id": "bfc2fac8", + "metadata": {}, + "source": [ + "## 12. Inspect the per-segment models\n", + "\n", + "Here we reconstruct the model on each segment and compare it directly to the data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "91250b4c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "best_result = results[-1]\n", + "best_collection = uvb_vis_nir\n", + "\n", + "model_full_list, coeffs_list, used_masks, excluded_masks = reconstruct_phoenix_legendre_models_for_segments(\n", + " best_collection,\n", + " phoenix_lib=phoenix_lib,\n", + " fit_result=best_result,\n", + " exclude_mask=uvb_exclude_mask,\n", + " mdeg=1,\n", + " forward_model=\"native_interp\",\n", + ")\n", + "\n", + "nseg = len(best_collection)\n", + "fig, axes = plt.subplots(nseg, 1, figsize=(12, 2.8 * nseg), sharex=False)\n", + "\n", + "if nseg == 1:\n", + " axes = [axes]\n", + "\n", + "for ax, seg, model_full, used_mask in zip(axes, best_collection, model_full_list, used_masks):\n", + " ax.plot(seg.wave, seg.flux, lw=0.8, label=\"data\")\n", + " ax.plot(seg.wave, model_full, lw=1.2, label=\"model\")\n", + " if np.any(used_mask):\n", + " ax.axvspan(seg.wave[used_mask].min(), seg.wave[used_mask].max(), alpha=0.12)\n", + " ax.set_title(seg.name)\n", + " ax.set_ylabel(\"Flux\")\n", + " ax.legend(loc=\"best\")\n", + "\n", + "axes[-1].set_xlabel(\"Wavelength [Å]\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "c93b5155", + "metadata": {}, + "source": [ + "## 13. Print a compact summary\n", + "\n", + "This gives a short text summary that is easy to copy into notes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "dfbfc910", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "========================================================================\n", + "UVB only\n", + " Teff = 10801.563201130215\n", + " [Fe/H] = -0.4346978238435111\n", + " logg = 3.0662900962500554\n", + " RV [km/s] = -10.571000720249149\n", + " chi2_red = 2.2646471441152567\n", + " n_segments= 3\n", + " names = ['UVB_Hdelta', 'UVB_Hgamma', 'UVB_Hbeta']\n", + " weights = [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n", + "========================================================================\n", + "UVB + VIS\n", + " Teff = 11778.9517756092\n", + " [Fe/H] = -0.49738260477028706\n", + " logg = 3.18393330893306\n", + " RV [km/s] = -3.047971441136603\n", + " chi2_red = 2.764991823807381\n", + " n_segments= 4\n", + " names = ['UVB_Hdelta', 'UVB_Hgamma', 'UVB_Hbeta', 'VIS_Halpha']\n", + " weights = [0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 1.0]\n", + "========================================================================\n", + "UVB + VIS + NIR\n", + " Teff = 11767.683610173082\n", + " [Fe/H] = -0.4999999997235279\n", + " logg = 3.2009622329843683\n", + " RV [km/s] = -1.0781207187215807\n", + " chi2_red = 2.912431747230757\n", + " n_segments= 5\n", + " names = ['UVB_Hdelta', 'UVB_Hgamma', 'UVB_Hbeta', 'VIS_Halpha', 'NIR_Pabeta']\n", + " weights = [0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 1.0, 1.0]\n" + ] + } + ], + "source": [ + "for res in results:\n", + " print(\"=\" * 72)\n", + " print(res[\"label\"])\n", + " print(\" Teff =\", res[\"teff\"])\n", + " print(\" [Fe/H] =\", res[\"feh\"])\n", + " print(\" logg =\", res[\"logg\"])\n", + " print(\" RV [km/s] =\", res[\"rv_kms\"])\n", + " print(\" chi2_red =\", res[\"chi2_red\"])\n", + " print(\" n_segments=\", res[\"n_segments\"])\n", + " print(\" names =\", res[\"segment_names\"])\n", + " print(\" weights =\", res[\"segment_weights\"])\n" + ] + }, + { + "cell_type": "markdown", + "id": "4c0c5f35", + "metadata": {}, + "source": [ + "## 14. Interpretation and caveats\n", + "\n", + "This notebook is an advanced worked example, not a final precision-analysis template.\n", + "\n", + "A few points to keep in mind:\n", + "\n", + "- the fitter now uses the segment-specific broadening metadata carried by each selected segment\n", + "- the VIS and NIR products here are telluric-corrected, but residuals can still matter\n", + "- the selected VIS and NIR windows are first-pass choices and can be edited\n", + "- different arms do not always constrain the model in the same way, so it is important to inspect the per-segment residuals and compare how the inferred parameters change when new windows are added\n", + "\n", + "Why can the UVB-only and multi-arm results differ?\n", + "\n", + "The UVB fit is driven mainly by the broad Balmer lines Hdelta, Hgamma, and Hbeta. These are usually the cleanest and most informative diagnostics in this example, especially in their wings. When VIS and NIR windows are added, the fit also tries to match Halpha and the selected near-infrared hydrogen lines. Those extra windows can add useful information, but they can also introduce extra sensitivity to continuum placement, telluric residuals, wavelength-convention choices, and the local broadening assumptions.\n", + "\n", + "A fit that uses more windows is therefore not automatically more reliable. It is solving a broader problem, under more assumptions.\n", + "\n", + "Which result should you trust more?\n", + "\n", + "For a case like this, the UVB-only fit is a sensible baseline because it is driven by the Balmer wings that carry most of the temperature and gravity information. The mixed-arm fit should be treated as a consistency test unless the added VIS and NIR windows improve the fit in a stable and physically sensible way.\n", + "\n", + "In practice, a good workflow is:\n", + "\n", + "1. fit the UVB windows alone and inspect the residuals\n", + "2. add the VIS window and see how much the solution shifts\n", + "3. add the NIR windows and compare again\n", + "4. check whether the added windows improve the fit without introducing obvious new systematic mismatches\n", + "\n", + "What should you look for?\n", + "\n", + "Small changes in the fitted parameters are reassuring. Large shifts are a sign to look more carefully at the assumptions. In particular, it is worth checking:\n", + "\n", + "- whether the result is stable if you adjust the window boundaries slightly\n", + "- whether the answer changes when you widen or narrow the masked line cores\n", + "- whether one arm still shows structured residuals after the fit\n", + "- whether the inferred solution depends strongly on the chosen segment weights\n", + "- whether the broadening and wavelength conventions are being handled consistently across arms\n", + "\n", + "If the parameters change only slightly and the residuals improve across the added segments, the multi-arm result is more persuasive. If the parameters shift strongly or one arm still shows clear systematic residuals, then the UVB-only result is often the cleaner baseline while the mixed-arm setup still needs refinement.\n", + "\n", + "For this example, the UVB Balmer-wing result is the natural reference point. The mixed-arm result is most useful as an exploratory extension of that baseline and as a check on how much additional windows pull the solution.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/cache_rebuild_smoketest.py b/scripts/cache_rebuild_smoketest.py new file mode 100644 index 0000000..e43f365 --- /dev/null +++ b/scripts/cache_rebuild_smoketest.py @@ -0,0 +1,162 @@ +import os +import argparse +import tempfile +import warnings +import numpy as np + +# pysynphot is legacy and emits a pkg_resources deprecation warning. +# Suppress it in smoke-test scripts to keep output readable. +warnings.filterwarnings( + "ignore", + message=r"pkg_resources is deprecated as an API.*", + category=UserWarning, + module=r"pysynphot.*", +) + +from Spyctres.phoenix import PhoenixLibrary +from Spyctres.fitting import fit_phoenix_full_spectrum +from Spyctres.io import SpectrumSegment +from Spyctres.config import load_user_config, get_config_value, resolve_setting + +def build_parser(): + return argparse.ArgumentParser( + description=( + "PHOENIX interpolator cache-rebuild smoke test.\n" + "This creates a synthetic spectrum, runs the fitter once to write a cache, " + "then runs it again with the same wavelength grid but a different Teff grid. " + "The second run should rebuild the cache and still succeed." + ), + epilog=( + "Examples:\n" + " export SPYCTRES_PHOENIX_DIR=/path/to/PHOENIXv2\n" + " python scripts/cache_rebuild_smoketest.py\n\n" + " python scripts/cache_rebuild_smoketest.py \\\n" + " --phoenix-dir /path/to/PHOENIXv2 \\\n" + " --cache-path /tmp/spyctres_cache_rebuild_test.npz\n" + " ~/.config/spyctres/config.toml:\n" + " [paths]\n" + " phoenix_dir = \"/path/to/PHOENIXv2\"\n" + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + +def make_synthetic_segment(phoenix_dir): + truth = dict(teff=5050.0, feh=-0.25, logg=4.25, rv=0.0) + + wave = np.linspace(5000.0, 5300.0, 2000) + + lib_truth = PhoenixLibrary(phoenix_dir, verbose=False) + lib_truth.build_interpolator( + observed_wave=wave, + teff_grid=np.array([4800.0, 5000.0, 5200.0, 5400.0]), + feh_grid=np.array([-0.5, 0.0]), + logg_grid=np.array([4.0, 4.5]), + cache_path=None, + allow_missing=False, + ) + + flux = np.asarray(lib_truth.evaluate(truth["teff"], truth["feh"], truth["logg"]), dtype=float) + err = np.full_like(flux, 0.02 * np.median(np.abs(flux))) + rng = np.random.default_rng(12345) + noisy_flux = flux + rng.normal(0.0, err, size=flux.size) + + seg = SpectrumSegment( + wave=wave, + flux=noisy_flux, + err=err, + mask=np.isfinite(noisy_flux) & np.isfinite(err) & (err > 0), + meta={"instrument": "synthetic"}, + wave_medium="vacuum", + wave_frame="stellar_rest", + name="synthetic_cache_rebuild_test", + ) + return seg + + +def run_fit(seg, phoenix_dir, cache_path, teff_grid, label): + print("\n=== {0} ===".format(label)) + lib = PhoenixLibrary(phoenix_dir, verbose=True) + + result = fit_phoenix_full_spectrum( + segments=[seg], + phoenix_lib=lib, + p0=(5050.0, -0.25, 4.25, 0.0), + teff_grid=np.asarray(teff_grid, dtype=float), + feh_grid=np.array([-0.5, 0.0]), + logg_grid=np.array([4.0, 4.5]), + cache_path=cache_path, + rv_init="grid", + mdeg=2, + verbose=False, + ) + + print( + "best:", + { + "teff": result["teff"], + "feh": result["feh"], + "logg": result["logg"], + "rv": result["rv_kms"], + }, + ) + print("chi2_red:", result["chi2_red"]) + + +def main(): + parser = build_parser() + parser.add_argument( + "--phoenix-dir", + default=None, + help="Path to local PHOENIXv2 directory. Precedence: CLI > SPYCTRES_PHOENIX_DIR > config file.", + ) + parser.add_argument( + "--cache-path", + default=os.path.join(tempfile.gettempdir(), "spyctres_cache_rebuild_test.npz"), + help="Cache path used for the rebuild test.", + ) + args = parser.parse_args() + config = load_user_config() + phoenix_dir_cfg = get_config_value(config, "paths", "phoenix_dir", default=None) + + args.phoenix_dir = resolve_setting( + args.phoenix_dir, + env_var_name="SPYCTRES_PHOENIX_DIR", + config_value=phoenix_dir_cfg, + default=None, + ) + if args.phoenix_dir is None: + parser.error( + "No PHOENIX directory supplied. Set --phoenix-dir, SPYCTRES_PHOENIX_DIR, " + "or [paths].phoenix_dir in ~/.config/spyctres/config.toml." + ) + + if not os.path.isdir(args.phoenix_dir): + parser.error("PHOENIX directory not found: {0}".format(args.phoenix_dir)) + + cache_path = args.cache_path + + if os.path.exists(cache_path): + os.remove(cache_path) + + seg = make_synthetic_segment(args.phoenix_dir) + + run_fit( + seg, + args.phoenix_dir, + cache_path, + teff_grid=[4800.0, 5000.0, 5200.0, 5400.0], + label="FIRST RUN", + ) + + run_fit( + seg, + args.phoenix_dir, + cache_path, + teff_grid=[4700.0, 4900.0, 5100.0, 5300.0], + label="SECOND RUN WITH DIFFERENT TEFF GRID", + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/fitting_smoketest.py b/scripts/fitting_smoketest.py new file mode 100644 index 0000000..bac1e0e --- /dev/null +++ b/scripts/fitting_smoketest.py @@ -0,0 +1,246 @@ +import os +import argparse +import warnings +# pysynphot is legacy and emits a pkg_resources deprecation warning. +# Suppress it in smoke-test scripts to keep output readable. +warnings.filterwarnings( + "ignore", + message=r"pkg_resources is deprecated as an API.*", + category=UserWarning, + module=r"pysynphot.*", +) + +import numpy as np + +from Spyctres.phoenix import PhoenixLibrary +from Spyctres.io import SpectrumSegment +from Spyctres.fitting import ( + fit_phoenix_full_spectrum, + _solve_multiplicative_legendre, + _gaussian_broaden_velocity, + _apply_observed_grid_rv_shift, +) +from Spyctres.config import load_user_config, get_config_value, resolve_setting + +def build_parser(): + return argparse.ArgumentParser( + description=( + "Synthetic PHOENIX full-spectrum fitter smoke test.\n" + "This builds a small synthetic spectrum from the PHOENIX library, " + "adds noise and a mild continuum tilt, then checks whether " + "fit_phoenix_full_spectrum() recovers the planted parameters." + ), + epilog=( + "Examples:\n" + " export SPYCTRES_PHOENIX_DIR=/path/to/PHOENIXv2\n" + " python scripts/fitting_smoketest.py\n\n" + " python scripts/fitting_smoketest.py \\\n" + " --phoenix-dir /path/to/PHOENIXv2 \\\n" + " --cache-path /tmp/spyctres_fit_cache.npz \\\n" + " --verbose 2\n\n" + " ~/.config/spyctres/config.toml:\n" + " [paths]\n" + " phoenix_dir = \"/path/to/PHOENIXv2\"\n" + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + +def chi2_red_with_poly(lib, wave, flux_n, err, teff, feh, logg, rv, fwhm_kms, mdeg=2): + m0 = lib.evaluate(teff, feh, logg) + sh = _apply_observed_grid_rv_shift(wave, m0, rv) + sh = _gaussian_broaden_velocity(wave, sh, fwhm_kms=fwhm_kms) + m_corr, coeffs = _solve_multiplicative_legendre(wave, flux_n, err, sh, mdeg=mdeg) + r = (flux_n - m_corr) / err + chi2 = float(np.sum(r * r)) + dof = max(1, len(r) - 4) + return chi2 / dof, coeffs + + +def main(): + parser = build_parser() + parser.add_argument( + "--phoenix-dir", + default=None, + help="Path to local PHOENIXv2 directory. Precedence: CLI > SPYCTRES_PHOENIX_DIR > config file.", + ) + parser.add_argument( + "--cache-path", + default="/tmp/spyctres_fit_cache.npz", + help="Cache path for the observed-grid PHOENIX interpolator.", + ) + parser.add_argument( + "--wave-min", + type=float, + default=6500.0, + help="Minimum wavelength in Angstrom for the synthetic chunk.", + ) + parser.add_argument( + "--wave-max", + type=float, + default=6600.0, + help="Maximum wavelength in Angstrom for the synthetic chunk.", + ) + parser.add_argument( + "--stride", + type=int, + default=5, + help="Subsampling stride applied to the native PHOENIX wavelength grid.", + ) + parser.add_argument( + "--R", + type=float, + default=50000.0, + help="Resolving power used to broaden the synthetic spectrum.", + ) + parser.add_argument( + "--seed", + type=int, + default=0, + help="Random seed for synthetic noise generation.", + ) + parser.add_argument( + "--noise-frac", + type=float, + default=0.01, + help="Noise sigma as a fraction of the median noiseless flux.", + ) + parser.add_argument( + "--verbose", + type=int, + default=2, + help="Verbosity passed to fit_phoenix_full_spectrum().", + ) + args = parser.parse_args() + config = load_user_config() + phoenix_dir_cfg = get_config_value(config, "paths", "phoenix_dir", default=None) + + args.phoenix_dir = resolve_setting( + args.phoenix_dir, + env_var_name="SPYCTRES_PHOENIX_DIR", + config_value=phoenix_dir_cfg, + default=None, + ) + + if args.phoenix_dir is None: + parser.error( + "No PHOENIX directory supplied. Set --phoenix-dir, SPYCTRES_PHOENIX_DIR, " + "or [paths].phoenix_dir in ~/.config/spyctres/config.toml." + ) + + if not os.path.isdir(args.phoenix_dir): + parser.error("PHOENIX directory not found: {0}".format(args.phoenix_dir)) + + if args.wave_max <= args.wave_min: + parser.error("--wave-max must be greater than --wave-min.") + + if args.stride < 1: + parser.error("--stride must be >= 1.") + + if args.R <= 0: + parser.error("--R must be positive.") + + if args.noise_frac <= 0: + parser.error("--noise-frac must be positive.") + + r_test = float(args.R) + fwhm_test = 299792.458 / r_test + + # Build a small wavelength chunk for a fast test + lib = PhoenixLibrary(args.phoenix_dir, verbose=True) + w = lib.phoenix_wave + mask = (w > args.wave_min) & (w < args.wave_max) + wave = w[mask][::args.stride] + # PHOENIX native wavelengths are vacuum wavelengths. + + if wave.size < 10: + parser.error( + "Selected wavelength chunk is too small after masking/stride. " + "Adjust --wave-min/--wave-max/--stride." + ) + + # Tiny grid around the truth + teff_grid = np.array([4900, 5000, 5100], dtype=float) + feh_grid = np.array([-0.5, 0.0], dtype=float) + logg_grid = np.array([4.0, 4.5], dtype=float) + + lib.build_interpolator( + observed_wave=wave, + teff_grid=teff_grid, + feh_grid=feh_grid, + logg_grid=logg_grid, + cache_path=args.cache_path, + allow_missing=False, + ) + + # Synthetic truth (must lie inside the grid bounds) + truth = dict(teff=5050.0, feh=-0.25, logg=4.25, rv=12.3) + + # Generate synthetic data using the same shift operator used in fitting + model0 = lib.evaluate(truth["teff"], truth["feh"], truth["logg"]) + shifted = _apply_observed_grid_rv_shift(wave, model0, truth["rv"]) + shifted = _gaussian_broaden_velocity(wave, shifted, fwhm_kms=fwhm_test) + + # Mild multiplicative continuum tilt + x = 2.0 * (wave - wave.min()) / (wave.max() - wave.min()) - 1.0 + cont = 1.0 + 0.02 * x + flux = shifted * cont + + # Add noise + sigma = float(args.noise_frac) * float(np.median(flux)) + rng = np.random.RandomState(args.seed) + flux_n = flux + rng.normal(0.0, sigma, size=flux.size) + err = np.ones_like(flux_n) * sigma + + seg = SpectrumSegment( + wave, + flux_n, + err=err, + wave_medium="vacuum", + name="synthetic", + ) + + chi2t, ct = chi2_red_with_poly( + lib, wave, flux_n, err, + truth["teff"], truth["feh"], truth["logg"], truth["rv"], + fwhm_test, mdeg=2, + ) + print("chi2_red at TRUTH:", chi2t, "poly coeffs:", ct) + + # RV shift sanity check + m0 = lib.evaluate(truth["teff"], truth["feh"], truth["logg"]) + sh0 = _apply_observed_grid_rv_shift(wave, m0, 0.0) + sh1 = _apply_observed_grid_rv_shift(wave, m0, truth["rv"]) + print("RV effect (max |Δ| / median):", float(np.max(np.abs(sh1 - sh0)) / np.median(np.abs(sh0)))) + + # Fit + p0 = (5000.0, 0.0, 4.0, 0.0) + bounds = ((4900.0, -0.5, 4.0, -50.0), (5100.0, 0.0, 4.5, 50.0)) + + out = fit_phoenix_full_spectrum( + [seg], + phoenix_lib=lib, + p0=p0, + R=r_test, + bounds=bounds, + regions=None, + mdeg=2, + rv_bary_kms=0.0, + verbose=args.verbose, + max_nfev=500, + ) + + print("Truth:", truth) + print("Best :", dict(teff=out["teff"], feh=out["feh"], logg=out["logg"], rv=out["rv_kms"])) + print("chi2_red (reported):", out["chi2_red"]) + + chi2b, cb = chi2_red_with_poly( + lib, wave, flux_n, err, + out["teff"], out["feh"], out["logg"], out["rv_kms"], + fwhm_test, mdeg=2, + ) + print("chi2_red at BEST :", chi2b, "poly coeffs:", cb) + + +if __name__ == "__main__": + main() diff --git a/scripts/floyds_fit_smoketest.py b/scripts/floyds_fit_smoketest.py new file mode 100644 index 0000000..1b2799d --- /dev/null +++ b/scripts/floyds_fit_smoketest.py @@ -0,0 +1,247 @@ +import os +import argparse +import warnings + +# pysynphot is legacy and emits a pkg_resources deprecation warning. +# Suppress it in smoke-test scripts to keep output readable. +warnings.filterwarnings( + "ignore", + message=r"pkg_resources is deprecated as an API.*", + category=UserWarning, + module=r"pysynphot.*", +) + +import numpy as np +import matplotlib.pyplot as plt + +from Spyctres.config import load_user_config, get_config_value, resolve_setting +from Spyctres.io import read_spectrum +from Spyctres.phoenix import PhoenixLibrary +from Spyctres.fitting import ( + fit_phoenix_full_spectrum, + reconstruct_phoenix_legendre_models_for_segments, +) +from Spyctres.plotting import plot_full_spectrum_fit +from Spyctres.recipes import pick_grid_range + + +def build_parser(): + return argparse.ArgumentParser( + description=( + "Quick PHOENIX fit smoke test for a reduced 1D FLOYDS spectrum.\n" + "This is a first-pass quicklook fitter for low-resolution FLOYDS data.\n" + "It fits a selected wavelength range with a multiplicative polynomial continuum." + ), + epilog=( + "Examples:\n" + " python scripts/floyds_fit_smoketest.py /path/to/FLOYDS.csv\n\n" + " python scripts/floyds_fit_smoketest.py \\\n" + " --wave-medium vacuum \\\n" + " --forward-model native_interp \\\n" + " --wmin 3800 --wmax 5600 \\\n" + " /path/to/FLOYDS.csv\n\n" + " ~/.config/spyctres/config.toml:\n" + " [paths]\n" + " phoenix_dir = \"/path/to/PHOENIXv2\"\n" + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + +def main(): + parser = build_parser() + parser.add_argument("file", help="Input FLOYDS CSV spectrum") + parser.add_argument( + "--phoenix-dir", + default=None, + help="Path to local PHOENIXv2 directory. Precedence: CLI > SPYCTRES_PHOENIX_DIR > config file.", + ) + parser.add_argument( + "--wave-medium", + choices=["unknown", "air", "vacuum"], + default="unknown", + help="Wavelength medium hypothesis for the observed spectrum.", + ) + parser.add_argument( + "--forward-model", + choices=["interp_observed", "native_interp"], + default="native_interp", + help="Forward-model path. For unknown wavelength medium, prefer native_interp.", + ) + parser.add_argument( + "--model-margin", + type=float, + default=200.0, + help="Margin in Angstrom for native_interp model preparation.", + ) + parser.add_argument("--wmin", type=float, default=3800.0, help="Minimum wavelength in Angstrom") + parser.add_argument("--wmax", type=float, default=5600.0, help="Maximum wavelength in Angstrom") + parser.add_argument("--clip-left", type=int, default=0, help="Clip this many pixels from the left edge") + parser.add_argument("--clip-right", type=int, default=0, help="Clip this many pixels from the right edge") + parser.add_argument("--R-override", type=float, default=None, help="Override metadata resolving power R") + parser.add_argument("--teff-min", type=float, default=7000.0, help="Minimum Teff for explicit PHOENIX grid") + parser.add_argument("--teff-max", type=float, default=12000.0, help="Maximum Teff for explicit PHOENIX grid") + parser.add_argument("--feh-min", type=float, default=-1.0, help="Minimum [Fe/H] for explicit PHOENIX grid") + parser.add_argument("--feh-max", type=float, default=0.5, help="Maximum [Fe/H] for explicit PHOENIX grid") + parser.add_argument("--logg-min", type=float, default=2.5, help="Minimum logg for explicit PHOENIX grid") + parser.add_argument("--logg-max", type=float, default=5.5, help="Maximum logg for explicit PHOENIX grid") + parser.add_argument("--mdeg", type=int, default=3, help="Legendre continuum degree") + parser.add_argument("--teff0", type=float, default=9500.0, help="Initial Teff") + parser.add_argument("--feh0", type=float, default=-0.5, help="Initial [Fe/H]") + parser.add_argument("--logg0", type=float, default=4.0, help="Initial logg") + parser.add_argument("--rv0", type=float, default=0.0, help="Initial stellar RV in km/s") + parser.add_argument("--rv-init", choices=["grid", "none"], default="grid", help="RV initialization strategy") + parser.add_argument("--rv-grid-n", type=int, default=161, help="Number of trial RV points in coarse RV scan") + parser.add_argument("--cache-path", default="/tmp/spyctres_floyds_fit_cache.npz") + parser.add_argument("--verbose", type=int, default=1) + args = parser.parse_args() + + config = load_user_config() + phoenix_dir_cfg = get_config_value(config, "paths", "phoenix_dir", default=None) + + args.phoenix_dir = resolve_setting( + args.phoenix_dir, + env_var_name="SPYCTRES_PHOENIX_DIR", + config_value=phoenix_dir_cfg, + default=None, + ) + + if not os.path.isfile(args.file): + parser.error("Input file not found: {0}".format(args.file)) + + if args.phoenix_dir is None: + parser.error( + "No PHOENIX directory supplied. Set --phoenix-dir, SPYCTRES_PHOENIX_DIR, " + "or [paths].phoenix_dir in ~/.config/spyctres/config.toml." + ) + + if not os.path.isdir(args.phoenix_dir): + parser.error("PHOENIX directory not found: {0}".format(args.phoenix_dir)) + + if args.wmax <= args.wmin: + parser.error("--wmax must be greater than --wmin.") + + if args.forward_model == "interp_observed" and args.wave_medium == "unknown": + parser.error( + "--forward-model interp_observed requires a known --wave-medium. " + "Use --wave-medium air or vacuum, or use --forward-model native_interp." + ) + + seg0 = read_spectrum(args.file, instrument="floyds") + + if args.wave_medium != "unknown": + meta = dict(seg0.meta) + meta["wave_medium"] = args.wave_medium + seg0 = seg0.copy(meta=meta, wave_medium=args.wave_medium) + + seg = seg0.window( + wmin=args.wmin, + wmax=args.wmax, + clip_left=args.clip_left, + clip_right=args.clip_right, + name_suffix="fitwin", + ) + + if seg.err is None: + print("WARNING: no uncertainty column found; chi2_red is only heuristic in this quicklook fit.") + + phoenix_lib = PhoenixLibrary(args.phoenix_dir, verbose=bool(args.verbose)) + + teff_avail, feh_avail, logg_avail = phoenix_lib.available_axes() + teff_grid_req = pick_grid_range(teff_avail, args.teff_min, args.teff_max) + feh_grid_req = pick_grid_range(feh_avail, args.feh_min, args.feh_max) + logg_grid_req = pick_grid_range(logg_avail, args.logg_min, args.logg_max) + + teff_grid_fit, feh_grid_fit, logg_grid_fit = phoenix_lib.complete_subgrid( + teff_grid_req, feh_grid_req, logg_grid_req + ) + + R = args.R_override if args.R_override is not None else seg.meta.get("resolution_R", None) + + out = fit_phoenix_full_spectrum( + [seg], + phoenix_lib=phoenix_lib, + p0=(args.teff0, args.feh0, args.logg0, args.rv0), + exclude_mask=None, + mdeg=args.mdeg, + rv_bary_kms=0.0, + R=R, + forward_model=args.forward_model, + model_margin_A=args.model_margin, + teff_grid=teff_grid_fit, + feh_grid=feh_grid_fit, + logg_grid=logg_grid_fit, + cache_path=args.cache_path, + rv_init=None if args.rv_init == "none" else "grid", + rv_grid_n=args.rv_grid_n, + verbose=args.verbose, + max_nfev=300, + ) + + model_list, coeffs_list, used_masks, excluded_masks = reconstruct_phoenix_legendre_models_for_segments( + segments=[seg], + phoenix_lib=phoenix_lib, + fit_result=out, + exclude_mask=None, + mdeg=args.mdeg, + rv_bary_kms=0.0, + R=R, + fwhm_kms=None, + forward_model=args.forward_model, + model_margin_A=args.model_margin, + ) + + print("File:", args.file) + print("Object:", seg.name) + print("Instrument:", seg.meta.get("instrument")) + print("Facility:", seg.meta.get("facility")) + print("Date obs:", seg.meta.get("date_obs")) + print("Wave medium:", seg.wave_medium) + print("Wave frame:", seg.wave_frame) + print("Pixels used:", int(np.sum(used_masks[0])), "/", len(seg.wave)) + print("Window [A]:", (args.wmin, args.wmax)) + print("R used:", R) + print("Teff grid used:", teff_grid_fit) + print("FeH grid used:", feh_grid_fit) + print("logg grid used:", logg_grid_fit) + print("Best-fit:") + print(" Teff =", out["teff"]) + print(" [Fe/H] =", out["feh"]) + print(" logg =", out["logg"]) + print(" RV =", out["rv_kms"]) + print(" chi2 =", out["chi2"]) + print(" dof =", out["dof"]) + print(" chi2_red =", out["chi2_red"]) + print(" success =", out["success"]) + print(" message =", out["message"]) + print("Continuum coeffs:", coeffs_list[0]) + + title = ( + "{0} {1:.0f}-{2:.0f} A Teff={3:.0f} [Fe/H]={4:.2f} " + "logg={5:.2f} RV={6:.1f} chi2_red={7:.2f}".format( + os.path.basename(args.file), + args.wmin, + args.wmax, + out["teff"], + out["feh"], + out["logg"], + out["rv_kms"], + out["chi2_red"], + ) + ) + + fig, axes = plot_full_spectrum_fit( + wave=seg.wave, + flux=seg.flux, + err=seg.err, + model=model_list[0], + used_mask=used_masks[0], + excluded_mask=excluded_masks[0], + title=title, + line_groups=["balmer", "caii", "hei"], + ) + plt.show() + + +if __name__ == "__main__": + main() diff --git a/scripts/gemini_fit_smoketest.py b/scripts/gemini_fit_smoketest.py new file mode 100644 index 0000000..33bfe86 --- /dev/null +++ b/scripts/gemini_fit_smoketest.py @@ -0,0 +1,325 @@ +import os +import argparse +import warnings + +# pysynphot is legacy and emits a pkg_resources deprecation warning. +# Suppress it in smoke-test scripts to keep output readable. +warnings.filterwarnings( + "ignore", + message=r"pkg_resources is deprecated as an API.*", + category=UserWarning, + module=r"pysynphot.*", +) + +import numpy as np +import matplotlib.pyplot as plt + +from Spyctres.config import load_user_config, get_config_value, resolve_setting +from Spyctres.io import read_spectrum +from Spyctres.phoenix import PhoenixLibrary +from Spyctres.fitting import ( + fit_phoenix_full_spectrum, + reconstruct_phoenix_legendre_models_for_segments, +) +from Spyctres.plotting import plot_full_spectrum_fit +from Spyctres.recipes import pick_grid_range + +def dilate_bad_mask(bad, n_pix=3): + """ + Grow a bad-pixel mask by n_pix nearest-neighbour steps on each side. + """ + bad = np.asarray(bad, dtype=bool).copy() + n_pix = int(max(0, n_pix)) + if n_pix == 0 or bad.size == 0: + return bad + + grown = bad.copy() + for _ in range(n_pix): + tmp = grown.copy() + tmp[:-1] |= grown[1:] + tmp[1:] |= grown[:-1] + grown = tmp + return grown + + +def build_gemini_fit_mask(seg, gap_grow_pix=8, min_flux_frac=0.25): + """ + Build a stricter fit mask for Gemini/GMOS ASCII spectra. + + This excludes: + - non-finite points + - deep near-zero dropout regions, treated as instrumental gaps/bad regions + - the strongest optical telluric bands near 6870 A and 7600 A + + The bad mask is then dilated to avoid fitting the edges of discontinuities. + """ + wave = np.asarray(seg.wave, dtype=float) + flux = np.asarray(seg.flux, dtype=float) + + good = np.isfinite(wave) & np.isfinite(flux) + + if seg.err is not None: + err = np.asarray(seg.err, dtype=float) + good &= np.isfinite(err) & (err > 0) + + pos = flux[np.isfinite(flux) & (flux > 0)] + if pos.size > 0: + median_flux = float(np.nanmedian(pos)) + deep_gap = flux <= float(min_flux_frac) * median_flux + else: + deep_gap = np.zeros_like(flux, dtype=bool) + + # Strong telluric O2 bands + telluric = ( + ((wave >= 6860.0) & (wave <= 6895.0)) | + ((wave >= 7590.0) & (wave <= 7640.0)) + ) + + bad = (~good) | deep_gap | telluric + bad = dilate_bad_mask(bad, n_pix=gap_grow_pix) + + good &= ~bad + return good + + +def build_parser(): + return argparse.ArgumentParser( + description=( + "Quick PHOENIX fit smoke test for a reduced 1D Gemini/GMOS ASCII spectrum.\n" + "This is a first-pass quicklook fitter for GMOS data.\n" + "It masks obvious zero-flux gap regions, then fits a selected wavelength " + "range with a multiplicative polynomial continuum." + ), + epilog=( + "Examples:\n" + " python scripts/gemini_fit_smoketest.py /path/to/gmos_ascii.dat\n\n" + " python scripts/gemini_fit_smoketest.py \\\n" + " --wave-medium air \\\n" + " --forward-model native_interp \\\n" + " --wmin 4700 --wmax 8800 \\\n" + " /path/to/gmos_ascii.dat\n\n" + " ~/.config/spyctres/config.toml:\n" + " [paths]\n" + " phoenix_dir = \"/path/to/PHOENIXv2\"\n" + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + +def main(): + parser = build_parser() + parser.add_argument("file", help="Input Gemini/GMOS ASCII spectrum") + parser.add_argument( + "--phoenix-dir", + default=None, + help="Path to local PHOENIXv2 directory. Precedence: CLI > SPYCTRES_PHOENIX_DIR > config file.", + ) + parser.add_argument( + "--wave-medium", + choices=["unknown", "air", "vacuum"], + default="unknown", + help="Wavelength medium hypothesis for the observed spectrum.", + ) + parser.add_argument( + "--forward-model", + choices=["interp_observed", "native_interp"], + default="native_interp", + help="Forward-model path. For unknown wavelength medium, prefer native_interp.", + ) + parser.add_argument( + "--model-margin", + type=float, + default=200.0, + help="Margin in Angstrom for native_interp model preparation.", + ) + parser.add_argument("--wmin", type=float, default=4700.0, help="Minimum wavelength in Angstrom") + parser.add_argument("--wmax", type=float, default=8800.0, help="Maximum wavelength in Angstrom") + parser.add_argument("--clip-left", type=int, default=0, help="Clip this many pixels from the left edge") + parser.add_argument("--clip-right", type=int, default=0, help="Clip this many pixels from the right edge") + parser.add_argument("--gap-grow-pix", type=int, default=3, help="Grow bad gap masks by this many pixels") + parser.add_argument( + "--min-flux-frac", + type=float, + default=0.25, + help="Reject points with flux below this fraction of the positive-flux median.", + ) + parser.add_argument("--R-override", type=float, default=500.0, help="Override resolving power R") + parser.add_argument("--teff-min", type=float, default=7000.0, help="Minimum Teff for explicit PHOENIX grid") + parser.add_argument("--teff-max", type=float, default=12000.0, help="Maximum Teff for explicit PHOENIX grid") + parser.add_argument("--feh-min", type=float, default=-1.0, help="Minimum [Fe/H] for explicit PHOENIX grid") + parser.add_argument("--feh-max", type=float, default=0.5, help="Maximum [Fe/H] for explicit PHOENIX grid") + parser.add_argument("--logg-min", type=float, default=2.5, help="Minimum logg for explicit PHOENIX grid") + parser.add_argument("--logg-max", type=float, default=5.5, help="Maximum logg for explicit PHOENIX grid") + parser.add_argument("--mdeg", type=int, default=3, help="Legendre continuum degree") + parser.add_argument("--teff0", type=float, default=9500.0, help="Initial Teff") + parser.add_argument("--feh0", type=float, default=-0.5, help="Initial [Fe/H]") + parser.add_argument("--logg0", type=float, default=4.0, help="Initial logg") + parser.add_argument("--rv0", type=float, default=0.0, help="Initial stellar RV in km/s") + parser.add_argument("--rv-init", choices=["grid", "none"], default="grid", help="RV initialization strategy") + parser.add_argument("--rv-grid-n", type=int, default=161, help="Number of trial RV points in coarse RV scan") + parser.add_argument("--cache-path", default="/tmp/spyctres_gemini_fit_cache.npz") + parser.add_argument("--verbose", type=int, default=1) + args = parser.parse_args() + + config = load_user_config() + phoenix_dir_cfg = get_config_value(config, "paths", "phoenix_dir", default=None) + + args.phoenix_dir = resolve_setting( + args.phoenix_dir, + env_var_name="SPYCTRES_PHOENIX_DIR", + config_value=phoenix_dir_cfg, + default=None, + ) + + if not os.path.isfile(args.file): + parser.error("Input file not found: {0}".format(args.file)) + + if args.phoenix_dir is None: + parser.error( + "No PHOENIX directory supplied. Set --phoenix-dir, SPYCTRES_PHOENIX_DIR, " + "or [paths].phoenix_dir in ~/.config/spyctres/config.toml." + ) + + if not os.path.isdir(args.phoenix_dir): + parser.error("PHOENIX directory not found: {0}".format(args.phoenix_dir)) + + if args.wmax <= args.wmin: + parser.error("--wmax must be greater than --wmin.") + + if args.forward_model == "interp_observed" and args.wave_medium == "unknown": + parser.error( + "--forward-model interp_observed requires a known --wave-medium. " + "Use --wave-medium air or vacuum, or use --forward-model native_interp." + ) + + seg0 = read_spectrum(args.file, instrument="gemini") + + strict_mask = build_gemini_fit_mask( + seg0, + gap_grow_pix=args.gap_grow_pix, + min_flux_frac=args.min_flux_frac, + ) + seg0 = seg0.copy(mask=strict_mask) + + if args.wave_medium != "unknown": + meta = dict(seg0.meta) + meta["wave_medium"] = args.wave_medium + seg0 = seg0.copy(meta=meta, wave_medium=args.wave_medium) + + seg = seg0.window( + wmin=args.wmin, + wmax=args.wmax, + clip_left=args.clip_left, + clip_right=args.clip_right, + name_suffix="fitwin", + ) + + if not np.any(seg.mask): + raise ValueError("No usable points remain after Gemini gap masking and windowing.") + + if seg.err is None: + print("WARNING: no uncertainty column found; chi2_red is only heuristic in this quicklook fit.") + + phoenix_lib = PhoenixLibrary(args.phoenix_dir, verbose=bool(args.verbose)) + + teff_avail, feh_avail, logg_avail = phoenix_lib.available_axes() + teff_grid_req = pick_grid_range(teff_avail, args.teff_min, args.teff_max) + feh_grid_req = pick_grid_range(feh_avail, args.feh_min, args.feh_max) + logg_grid_req = pick_grid_range(logg_avail, args.logg_min, args.logg_max) + + teff_grid_fit, feh_grid_fit, logg_grid_fit = phoenix_lib.complete_subgrid( + teff_grid_req, feh_grid_req, logg_grid_req + ) + + R = args.R_override if args.R_override is not None else seg.meta.get("resolution_R", None) + + if R is None: + print("WARNING: no resolving power supplied; model is not instrumentally broadened.") + + out = fit_phoenix_full_spectrum( + [seg], + phoenix_lib=phoenix_lib, + p0=(args.teff0, args.feh0, args.logg0, args.rv0), + exclude_mask=None, + mdeg=args.mdeg, + rv_bary_kms=0.0, + R=R, + forward_model=args.forward_model, + model_margin_A=args.model_margin, + teff_grid=teff_grid_fit, + feh_grid=feh_grid_fit, + logg_grid=logg_grid_fit, + cache_path=args.cache_path, + rv_init=None if args.rv_init == "none" else "grid", + rv_grid_n=args.rv_grid_n, + verbose=args.verbose, + max_nfev=300, + ) + + model_list, coeffs_list, used_masks, excluded_masks = reconstruct_phoenix_legendre_models_for_segments( + segments=[seg], + phoenix_lib=phoenix_lib, + fit_result=out, + exclude_mask=None, + mdeg=args.mdeg, + rv_bary_kms=0.0, + R=R, + fwhm_kms=None, + forward_model=args.forward_model, + model_margin_A=args.model_margin, + ) + + print("File:", args.file) + print("Object:", seg.name) + print("Instrument:", seg.meta.get("instrument")) + print("Facility:", seg.meta.get("facility")) + print("Origin:", seg.meta.get("origin")) + print("Wave medium:", seg.wave_medium) + print("Wave frame:", seg.wave_frame) + print("Pixels used:", int(np.sum(used_masks[0])), "/", len(seg.wave)) + print("Window [A]:", (args.wmin, args.wmax)) + print("R used:", R) + print("Teff grid used:", teff_grid_fit) + print("FeH grid used:", feh_grid_fit) + print("logg grid used:", logg_grid_fit) + print("Best-fit:") + print(" Teff =", out["teff"]) + print(" [Fe/H] =", out["feh"]) + print(" logg =", out["logg"]) + print(" RV =", out["rv_kms"]) + print(" chi2 =", out["chi2"]) + print(" dof =", out["dof"]) + print(" chi2_red =", out["chi2_red"]) + print(" success =", out["success"]) + print(" message =", out["message"]) + print("Continuum coeffs:", coeffs_list[0]) + + title = ( + "{0} {1:.0f}-{2:.0f} A Teff={3:.0f} [Fe/H]={4:.2f} " + "logg={5:.2f} RV={6:.1f} chi2_red={7:.2f}".format( + os.path.basename(args.file), + args.wmin, + args.wmax, + out["teff"], + out["feh"], + out["logg"], + out["rv_kms"], + out["chi2_red"], + ) + ) + + fig, axes = plot_full_spectrum_fit( + wave=seg.wave, + flux=seg.flux, + err=seg.err, + model=model_list[0], + used_mask=used_masks[0], + excluded_mask=excluded_masks[0], + title=title, + line_groups=["balmer", "caii", "hei"], + ) + plt.show() + + +if __name__ == "__main__": + main() diff --git a/scripts/io_smoketest.py b/scripts/io_smoketest.py new file mode 100644 index 0000000..0898437 --- /dev/null +++ b/scripts/io_smoketest.py @@ -0,0 +1,119 @@ +import os +import argparse +import numpy as np +import matplotlib.pyplot as plt + +from Spyctres.io import read_spectrum, concatenate_segments +from Spyctres.plotting import plot_spectrum_quicklook + + +def summarize(seg): + mask = np.asarray(seg.mask, dtype=bool) + + msg = [ + "name={0}".format(seg.name), + "N={0}".format(len(seg.wave)), + "N_used={0}".format(int(np.sum(mask))), + "finite_flux_frac={0:.4f}".format(float(np.mean(np.isfinite(seg.flux)))), + "medium={0}".format(seg.wave_medium), + "frame={0}".format(seg.wave_frame), + "R={0}".format(seg.meta.get("resolution_R")), + ] + + if np.any(mask): + w = seg.wave[mask] + msg.append("wave=[{0:.2f},{1:.2f}]".format(float(w.min()), float(w.max()))) + + if seg.err is not None: + e = seg.err[mask] + msg.append("median_err={0:.4g}".format(float(np.nanmedian(e)))) + else: + msg.append("err=None") + else: + msg.append("wave=[no good pixels]") + msg.append("err=None" if seg.err is None else "median_err=nan") + + instrument = str(seg.meta.get("instrument", "")).strip().upper() + + if instrument == "PEPSI": + msg.append("fiber={0}".format(seg.meta.get("fiber"))) + msg.append("cd={0}".format(seg.meta.get("cross_disperser"))) + + if instrument in ["XSHOOTER", "X-SHOOTER"]: + msg.append("arm={0}".format(seg.meta.get("arm"))) + msg.append("slit={0}".format(seg.meta.get("slit_name"))) + msg.append("telluric_corrected={0}".format(seg.meta.get("telluric_corrected"))) + msg.append("barycorr_kms={0}".format(seg.meta.get("barycorr_kms"))) + + if instrument == "FLOYDS": + msg.append("facility={0}".format(seg.meta.get("facility"))) + msg.append("date_obs={0}".format(seg.meta.get("date_obs"))) + + if instrument in ["GMOS", "GEMINI"]: + msg.append("object={0}".format(seg.meta.get("object"))) + msg.append("filename={0}".format(seg.meta.get("filename"))) + msg.append("origin={0}".format(seg.meta.get("origin"))) + + return " ".join(msg) + + +def build_parser(): + return argparse.ArgumentParser( + description=( + "Read one or more reduced 1D spectra with the Spyctres I/O layer and " + "print a short summary. Use this to confirm that a file is recognized, " + "its metadata look sensible, and the quick-look plot renders." + ), + epilog=( + "Examples:\n" + " python scripts/io_smoketest.py --instrument xshooter path/to/xshooter_1d.fits\n" + " python scripts/io_smoketest.py --instrument pepsi path/to/seg1.dxt.nor path/to/seg2.dxt.nor path/to/seg3.dxt.nor --join\n" + " python scripts/io_smoketest.py --instrument floyds path/to/FLOYDS.csv\n" + " python scripts/io_smoketest.py --instrument gemini path/to/gmos_ascii.dat\n" + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + +def main(): + parser = build_parser() + parser.add_argument( + "files", + nargs="+", + help="Input spectrum file(s)", + ) + parser.add_argument( + "--instrument", + required=True, + choices=["pepsi", "xshooter", "floyds", "gemini"], + help="Instrument reader to use", + ) + parser.add_argument( + "--join", + action="store_true", + help="Concatenate segments and print a joined summary", + ) + args = parser.parse_args() + + missing = [p for p in args.files if not os.path.isfile(p)] + if missing: + parser.error("Input file(s) not found: {0}".format(", ".join(missing))) + + segs = [] + for p in args.files: + s = read_spectrum(p, instrument=args.instrument) + segs.append(s) + print(p) + print(summarize(s)) + + fig, ax = plot_spectrum_quicklook(s, use_mask=True, show_error=False) + plt.show() + + if args.join and len(segs) > 1: + joined = concatenate_segments(segs, sort=True, name="{0}_joined".format(args.instrument)) + print("\nJOINED") + print(summarize(joined)) + + +if __name__ == "__main__": + main() diff --git a/scripts/pepsi_fit_smoketest.py b/scripts/pepsi_fit_smoketest.py new file mode 100644 index 0000000..25776b2 --- /dev/null +++ b/scripts/pepsi_fit_smoketest.py @@ -0,0 +1,1022 @@ +import os +import sys +import argparse +import warnings + +# pysynphot is legacy and emits a pkg_resources deprecation warning. +# Suppress it in smoke-test scripts to keep output readable. +warnings.filterwarnings( + "ignore", + message=r"pkg_resources is deprecated as an API.*", + category=UserWarning, + module=r"pysynphot.*", +) + +import numpy as np +import matplotlib.pyplot as plt +from scipy.optimize import minimize + +from Spyctres import Spyctres +from Spyctres.config import load_user_config, get_config_value, resolve_setting +from Spyctres.io import read_spectrum, make_padded_window_segments, SpectrumCollection +from Spyctres.phoenix import PhoenixLibrary +from Spyctres.fitting import ( + fit_phoenix_full_spectrum, + build_effective_fit_mask, + reconstruct_phoenix_legendre_models_for_segments, +) +from Spyctres.plotting import plot_full_spectrum_fit +from Spyctres.recipes import ( + apply_pepsi_wave_hypothesis, + build_pepsi_normalized_mask, + build_pepsi_legacy_segments, + make_pepsi_legacy_cache_support_segments, + ensure_phoenix_native_interpolator_for_segments, + pick_grid_range, + evaluate_pepsi_legacy_max_models, + pepsi_legacy_max_likelihood_terms, +) + + +WINDOW_PRESETS = { + "blue_balmer": [ + ("blue_classification", 4285.0, 4490.0), + ], + "red_halpha": [ + ("6495 blend", 6485.0, 6505.0), + ("6545 region", 6535.0, 6555.0), + ("6561 region", 6551.0, 6571.0), + ], + "red_metals": [ + ("Ca I 6439", 6432.0, 6447.0), + ("6495 blend", 6488.0, 6502.0), + ("6591 blend", 6585.0, 6597.0), + ("Li I 6708", 6702.0, 6714.0), + ], + "caii_triplet": [ + ("Ca II 8498", 8492.0, 8504.0), + ("Ca II 8542", 8536.0, 8548.0), + ("Ca II 8662", 8656.0, 8668.0), + ("Mg I 8807", 8802.0, 8812.0), + ], +} + + +def build_parser(): + return argparse.ArgumentParser( + description=( + "PEPSI PHOENIX fitting smoke test.\n" + "\n" + "This script has two roles:\n" + " 1. quicklook: a generic one-file PEPSI full-spectrum/window fit;\n" + " 2. legacy_max: a PEPSI line-window comparison mode that mimics the\n" + " local model/max(model) normalization used in PEPSI line-window tests.\n" + "\n" + "For normal development, prefer one of the named presets. Expert flags\n" + "remain available for diagnostics and sensitivity tests." + ), + epilog=( + "Recommended examples:\n" + "\n" + " Fast PEPSI red-window regression test:\n" + " python scripts/pepsi_fit_smoketest.py \\\n" + " --preset pepsi_legacy_red_fast \\\n" + " examples/data/pepsir.20230603.009.dxt.nor \\\n" + " examples/data/pepsir.20230603.010.dxt.nor\n" + "\n" + " Slower full-grid PEPSI red-window diagnostic:\n" + " python scripts/pepsi_fit_smoketest.py \\\n" + " --preset pepsi_legacy_red_full \\\n" + " examples/data/pepsir.20230603.009.dxt.nor \\\n" + " examples/data/pepsir.20230603.010.dxt.nor\n" + "\n" + " One-file quicklook fit:\n" + " python scripts/pepsi_fit_smoketest.py \\\n" + " --preset pepsi_quicklook \\\n" + " examples/data/pepsir.20230603.010.dxt.nor\n" + "\n" + "Configuration:\n" + " export SPYCTRES_PHOENIX_DIR=/path/to/PHOENIXv2\n" + "\n" + "or use ~/.config/spyctres/config.toml:\n" + " [paths]\n" + " phoenix_dir = \"/path/to/PHOENIXv2\"\n" + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + +def choose_auto_window_preset(seg): + wmin = float(np.nanmin(seg.wave)) + wmax = float(np.nanmax(seg.wave)) + + if wmax < 5000.0: + return "blue_balmer" + + if 6200.0 < wmin < 7000.0 and wmax < 7500.0: + return "red_halpha" + + if wmin > 7300.0: + return "caii_triplet" + + raise ValueError( + "Could not infer an automatic PEPSI window preset from wavelength range " + "[{0:.1f}, {1:.1f}] A.".format(wmin, wmax) + ) + + +def parse_custom_windows(window_args): + """Parse repeated --window LABEL WMIN WMAX arguments.""" + out = [] + for item in window_args: + if len(item) != 3: + raise ValueError("Each --window must be: LABEL WMIN WMAX") + label, wmin, wmax = item + out.append((str(label), float(wmin), float(wmax))) + return out + + +def build_window_segments(seg, window_defs, pad=2.0): + segments = make_padded_window_segments( + seg, + [(wmin, wmax) for _, wmin, wmax in window_defs], + pad=pad, + name_prefix="line", + ) + for seg_i, (label, _wmin, _wmax) in zip(segments, window_defs): + seg_i.name = label + return segments + + +def _flag_present(argv, dest): + """ + Return True if the corresponding flag was explicitly given. + + This lets preset values behave like defaults: explicit flags win. + """ + flag = "--" + str(dest).replace("_", "-") + return any(arg == flag or arg.startswith(flag + "=") for arg in argv) + + +def apply_named_preset(args, argv): + """Apply a named PEPSI fitting preset, while keeping explicit flags in control.""" + preset = getattr(args, "preset", None) + if preset is None: + return args + + if preset == "pepsi_legacy_red_fast": + preset_values = { + "mode": "legacy_max", + "fast": True, + "wave_hypothesis": "air", + "forward_model": "native_interp", + "use_ssbvel": True, + "use_telluric_mask": True, + "teff0": 5500.0, + "feh0": -1.0, + "logg0": 3.0, + "rv0": -50.0, + } + elif preset == "pepsi_legacy_red_full": + preset_values = { + "mode": "legacy_max", + "fast": False, + "wave_hypothesis": "air", + "forward_model": "native_interp", + "use_ssbvel": True, + "use_telluric_mask": True, + "teff0": 5500.0, + "feh0": -1.0, + "logg0": 3.0, + "rv0": -50.0, + } + elif preset == "pepsi_quicklook": + preset_values = { + "mode": "quicklook", + "wave_hypothesis": "air", + "forward_model": "native_interp", + "use_ssbvel": True, + "use_telluric_mask": True, + } + else: + raise ValueError("Unknown PEPSI preset: {0}".format(preset)) + + for dest, value in preset_values.items(): + if not _flag_present(argv, dest): + setattr(args, dest, value) + + return args + + +def concat_with_gap(arrays, gap_value=np.nan, dtype=float): + """Concatenate arrays with a single separator element between them.""" + arrays = [np.asarray(a, dtype=dtype) for a in arrays] + if len(arrays) == 0: + return np.array([], dtype=dtype) + + out = [] + for i, arr in enumerate(arrays): + out.append(arr) + if i < len(arrays) - 1: + out.append(np.array([gap_value], dtype=dtype)) + return np.concatenate(out) + + +def concat_bool_with_gap(arrays): + """Concatenate boolean arrays with a False separator between windows.""" + arrays = [np.asarray(a, dtype=bool) for a in arrays] + if len(arrays) == 0: + return np.array([], dtype=bool) + + out = [] + for i, arr in enumerate(arrays): + out.append(arr) + if i < len(arrays) - 1: + out.append(np.array([False], dtype=bool)) + return np.concatenate(out) + + +def _make_unit_collection(segments, window_defs, args): + """ + Wrap PEPSI legacy line-window segments in a SpectrumCollection. + + The current PEPSI legacy fit uses unit weights, preserving the previous + list-based behavior. The collection wrapper gives us the same abstraction + used by the generic fitter and leaves a clean place for future per-window or + per-arm weights. + """ + weights = np.ones(len(segments), dtype=float) + return SpectrumCollection( + segments=segments, + weights=weights, + meta={ + "instrument": "PEPSI", + "mode": "legacy_max", + "wave_hypothesis": args.wave_hypothesis, + "legacy_windows_air": list(window_defs), + "legacy_flux_range": (float(args.legacy_flux_min), float(args.legacy_flux_max)), + }, + name="pepsi_legacy_windows", + ) + + +def run_legacy_pepsi_fit(args, parser): + """ + Run the PEPSI legacy-max comparison fit. + + This remains a script-level driver. The reusable PEPSI-specific + segment/window/cache-support construction and legacy likelihood pieces live + in Spyctres.recipes. The fitted line windows are wrapped in a + SpectrumCollection so the joint fit has the same container abstraction as the + generic full-spectrum fitter. + """ + if args.forward_model != "native_interp": + parser.error("--mode legacy_max currently requires --forward-model native_interp.") + + files = list(args.files) + for path in files: + if not os.path.isfile(path): + parser.error("Input file not found: {0}".format(path)) + + if args.phoenix_dir is None: + parser.error( + "No PHOENIX directory supplied. Set --phoenix-dir, SPYCTRES_PHOENIX_DIR, " + "or [paths].phoenix_dir in ~/.config/spyctres/config.toml." + ) + + if not os.path.isdir(args.phoenix_dir): + parser.error("PHOENIX directory not found: {0}".format(args.phoenix_dir)) + + raw_segments = [] + for path in files: + seg = read_spectrum(path, instrument="pepsi") + meta = dict(seg.meta) + meta["source_file"] = path + raw_segments.append(seg.copy(meta=meta)) + + input_segments, segments, window_defs = build_pepsi_legacy_segments( + raw_segments, + wave_hypothesis=args.wave_hypothesis, + centers_air=args.legacy_centers, + halfwidth_A=args.legacy_halfwidth, + flux_min=args.legacy_flux_min, + flux_max=args.legacy_flux_max, + window_pad_A=args.window_pad, + ) + + if args.use_telluric_mask: + _, telluric_mask = Spyctres.load_telluric_lines(args.telluric_threshold) + + def exclude_mask(wave): + return np.asarray(telluric_mask(wave)) > 0.5 + + segments = [ + seg.copy(mask=np.asarray(seg.mask, dtype=bool) & ~exclude_mask(seg.wave)) + for seg in segments + ] + + collection = _make_unit_collection(segments, window_defs, args) + segments = list(collection.segments) + segment_weights = np.asarray(collection.weights, dtype=float) + + phoenix_lib = PhoenixLibrary(args.phoenix_dir, verbose=bool(args.verbose)) + + teff_avail, feh_avail, logg_avail = phoenix_lib.available_axes() + teff_grid_req = pick_grid_range(teff_avail, args.teff_min, args.teff_max) + feh_grid_req = pick_grid_range(feh_avail, args.feh_min, args.feh_max) + logg_grid_req = pick_grid_range(logg_avail, args.logg_min, args.logg_max) + + teff_grid_fit, feh_grid_fit, logg_grid_fit = phoenix_lib.complete_subgrid( + teff_grid_req, + feh_grid_req, + logg_grid_req, + ) + + rv_bary_values = [] + for seg in input_segments: + ssbvel_mps = seg.meta.get("ssbvel_mps") + if args.use_ssbvel and ssbvel_mps is not None: + rv_bary_values.append(1.0e-3 * float(ssbvel_mps)) + + rv_bary_kms = float(np.nanmedian(rv_bary_values)) if rv_bary_values else 0.0 + + R = args.R_override + if R is None: + r_vals = [seg.meta.get("resolution_R", None) for seg in input_segments] + r_vals = [float(x) for x in r_vals if x is not None] + R = float(np.nanmedian(r_vals)) if r_vals else None + + if args.cache_path is None: + names = "_".join(os.path.basename(p).replace(".", "_") for p in files) + if len(names) > 80: + names = "legacy_pepsi_joint" + + speed_tag = "fast" if args.fast else "full" + args.cache_path = "/tmp/spyctres_{0}_{1}_{2}_legacy_cache.npz".format( + names, + args.wave_hypothesis, + speed_tag, + ) + + cache_support_segments = make_pepsi_legacy_cache_support_segments( + input_segments=input_segments, + window_defs_air=window_defs, + window_pad_A=args.window_pad, + ) + + model_wave_grid, model_wave_medium = ensure_phoenix_native_interpolator_for_segments( + segments=cache_support_segments, + phoenix_lib=phoenix_lib, + teff_grid=teff_grid_fit, + feh_grid=feh_grid_fit, + logg_grid=logg_grid_fit, + cache_path=args.cache_path, + model_margin_A=args.model_margin, + ) + + lo = np.array( + [ + np.min(teff_grid_fit), + np.min(feh_grid_fit), + np.min(logg_grid_fit), + float(args.rv_min), + float(args.legacy_log_scale_min), + ], + dtype=float, + ) + + hi = np.array( + [ + np.max(teff_grid_fit), + np.max(feh_grid_fit), + np.max(logg_grid_fit), + float(args.rv_max), + float(args.legacy_log_scale_max), + ], + dtype=float, + ) + + x0 = np.array( + [args.teff0, args.feh0, args.logg0, args.rv0, 0.0], + dtype=float, + ) + x0 = np.minimum(np.maximum(x0, lo), hi) + + def objective(x): + teff, feh, logg, rv, log_scale = map(float, x) + + try: + models = evaluate_pepsi_legacy_max_models( + phoenix_lib=phoenix_lib, + segments=segments, + model_wave_grid=model_wave_grid, + model_wave_medium=model_wave_medium, + teff=teff, + feh=feh, + logg=logg, + rv_kms=rv, + rv_bary_kms=rv_bary_kms, + R=R, + model_margin_A=args.model_margin, + ) + except Exception: + return 1.0e100 + + total = 0.0 + n_total = 0 + + for seg, weight, model in zip(segments, segment_weights, models): + val, n, _model_norm, _used = pepsi_legacy_max_likelihood_terms( + seg, + model, + log_err_scale=log_scale, + ) + + if not np.isfinite(val): + return 1.0e100 + + total += float(weight) * float(val) + n_total += int(n) + + if n_total == 0: + return 1.0e100 + + return total + + if args.rv_init != "none": + rv_grid = np.linspace(float(args.rv_min), float(args.rv_max), int(args.rv_grid_n)) + vals = [] + + for rv in rv_grid: + xt = x0.copy() + xt[3] = rv + vals.append(objective(xt)) + + ibest = int(np.nanargmin(vals)) + x0[3] = float(rv_grid[ibest]) + print("Legacy RV init grid best:", x0[3]) + + res = minimize( + objective, + x0, + method="L-BFGS-B", + bounds=list(zip(lo, hi)), + options={ + "maxiter": int(args.legacy_maxiter), + "ftol": 1e-8, + }, + ) + + teff, feh, logg, rv, log_scale = map(float, res.x) + + best_models_raw = evaluate_pepsi_legacy_max_models( + phoenix_lib=phoenix_lib, + segments=segments, + model_wave_grid=model_wave_grid, + model_wave_medium=model_wave_medium, + teff=teff, + feh=feh, + logg=logg, + rv_kms=rv, + rv_bary_kms=rv_bary_kms, + R=R, + model_margin_A=args.model_margin, + ) + + model_norm_list = [] + used_masks = [] + chi2 = 0.0 + npts = 0 + + for seg, weight, model in zip(segments, segment_weights, best_models_raw): + _nll, n, model_norm, used = pepsi_legacy_max_likelihood_terms( + seg, + model, + log_err_scale=log_scale, + ) + + e = np.asarray(seg.err, dtype=float)[used] * (10.0 ** log_scale) + r = (np.asarray(seg.flux, dtype=float)[used] - model_norm[used]) / e + + chi2 += float(weight) * float(np.sum(r * r)) + npts += int(n) + model_norm_list.append(model_norm) + used_masks.append(used) + + dof = max(1, npts - 5) + chi2_red = chi2 / dof + + print("Mode: legacy_max") + print("Collection:", collection.name) + print("Collection segments:", len(collection.segments)) + print("Files:") + for path in files: + print(" ", path) + + print("Wave medium hypothesis:", args.wave_hypothesis) + print("Barycorr used [km/s]:", rv_bary_kms) + print("Telluric mask:", bool(args.use_telluric_mask)) + print("R used:", R) + print("Legacy flux range:", (args.legacy_flux_min, args.legacy_flux_max)) + + print("Legacy windows defined in air:") + for label, wmin, wmax in window_defs: + print(" ", label, (wmin, wmax)) + + print("Windows actually fitted:") + for seg, weight in zip(segments, segment_weights): + working = seg.meta.get("legacy_window_working", None) + medium = seg.meta.get("legacy_window_medium", seg.wave_medium) + + extra = "" + if working is not None: + extra = " working_window_{0}={1}".format(medium, working[1:]) + + print( + " ", + seg.name, + "from", + os.path.basename(str(seg.meta.get("source_file", ""))), + "N=", + int(np.sum(seg.mask)), + "weight=", + float(weight), + extra, + ) + + print("Best-fit:") + print(" Teff =", teff) + print(" [Fe/H] =", feh) + print(" logg =", logg) + print(" RV =", rv) + print(" log10 error scale =", log_scale) + print(" error scale =", 10.0 ** log_scale) + print(" nll =", float(res.fun)) + print(" chi2 =", chi2) + print(" dof =", dof) + print(" chi2_red =", chi2_red) + print(" success =", bool(res.success)) + print(" message =", res.message) + + wave_plot = concat_with_gap([seg.wave for seg in segments], gap_value=np.nan, dtype=float) + flux_plot = concat_with_gap([seg.flux for seg in segments], gap_value=np.nan, dtype=float) + err_plot = concat_with_gap([seg.err * (10.0 ** log_scale) for seg in segments], gap_value=np.nan, dtype=float) + model_plot = concat_with_gap(model_norm_list, gap_value=np.nan, dtype=float) + used_plot = concat_bool_with_gap(used_masks) + excl_plot = np.zeros_like(used_plot, dtype=bool) + + title = ( + "PEPSI legacy_max {0} Teff={1:.0f} [Fe/H]={2:.2f} " + "logg={3:.2f} RV={4:.1f} chi2_red={5:.2f}".format( + args.wave_hypothesis, + teff, + feh, + logg, + rv, + chi2_red, + ) + ) + + fig, axes = plot_full_spectrum_fit( + wave=wave_plot, + flux=flux_plot, + err=err_plot, + model=model_plot, + used_mask=used_plot, + excluded_mask=excl_plot, + title=title, + line_groups=None, + ) + plt.show() + + +def main(): + parser = build_parser() + + standard = parser.add_argument_group("standard options") + expert = parser.add_argument_group("expert fitting controls") + legacy = parser.add_argument_group("legacy_max controls") + cache = parser.add_argument_group("cache and verbosity") + + standard.add_argument("files", nargs="+", help="Input PEPSI .dxt.nor file(s)") + standard.add_argument( + "--preset", + choices=["pepsi_quicklook", "pepsi_legacy_red_fast", "pepsi_legacy_red_full"], + default=None, + help=( + "Apply a named PEPSI preset. " + "Recommended: pepsi_legacy_red_fast for development, " + "pepsi_legacy_red_full for a slower red-window diagnostic, " + "or pepsi_quicklook for a single-file generic fit. " + "Explicit flags override preset values." + ), + ) + standard.add_argument( + "--phoenix-dir", + default=None, + help=( + "Path to local PHOENIXv2 directory. " + "Precedence: CLI > SPYCTRES_PHOENIX_DIR > config file." + ), + ) + standard.add_argument( + "--mode", + choices=["quicklook", "legacy_max"], + default="quicklook", + help=( + "Fit mode. Use quicklook for a generic one-file PEPSI fit, " + "or legacy_max for the local line-window PEPSI comparison mode." + ), + ) + standard.add_argument( + "--window-preset", + choices=["auto", "blue_balmer", "red_halpha", "red_metals", "caii_triplet"], + default="auto", + help="Window preset for quicklook mode.", + ) + standard.add_argument( + "--window", + nargs=3, + action="append", + metavar=("LABEL", "WMIN", "WMAX"), + help="Custom quicklook fit window. Can be given multiple times.", + ) + standard.add_argument( + "--use-telluric-mask", + action="store_true", + help="Apply Spyctres' built-in telluric mask.", + ) + standard.add_argument( + "--use-ssbvel", + action="store_true", + help="Use header SSBVEL as a barycentric correction term.", + ) + standard.add_argument( + "--fast", + action="store_true", + help=( + "Use a smaller PEPSI development grid and shorter optimization. " + "Intended for code testing, not final science results." + ), + ) + + expert.add_argument( + "--wave-hypothesis", + choices=["unknown", "air", "vacuum", "air_to_vac"], + default="air", + help="Observed wavelength-medium hypothesis.", + ) + expert.add_argument( + "--forward-model", + choices=["interp_observed", "native_interp"], + default="native_interp", + help=( + "Forward-model path. native_interp is the recommended path for " + "line-profile work." + ), + ) + expert.add_argument( + "--model-margin", + type=float, + default=20.0, + help="Margin in Angstrom for native_interp model preparation.", + ) + expert.add_argument( + "--window-pad", + type=float, + default=2.0, + help="Padding in Angstrom added around each PEPSI fit window.", + ) + expert.add_argument( + "--R-override", + type=float, + default=None, + help="Override metadata resolving power R.", + ) + expert.add_argument( + "--teff-min", + type=float, + default=4500.0, + help="Minimum Teff for explicit PHOENIX grid.", + ) + expert.add_argument( + "--teff-max", + type=float, + default=12000.0, + help="Maximum Teff for explicit PHOENIX grid.", + ) + expert.add_argument( + "--feh-min", + type=float, + default=-1.5, + help="Minimum [Fe/H] for explicit PHOENIX grid.", + ) + expert.add_argument( + "--feh-max", + type=float, + default=0.5, + help="Maximum [Fe/H] for explicit PHOENIX grid.", + ) + expert.add_argument( + "--logg-min", + type=float, + default=2.5, + help="Minimum logg for explicit PHOENIX grid.", + ) + expert.add_argument( + "--logg-max", + type=float, + default=5.5, + help="Maximum logg for explicit PHOENIX grid.", + ) + expert.add_argument( + "--mdeg", + type=int, + default=1, + help="Legendre continuum degree for quicklook mode.", + ) + expert.add_argument("--teff0", type=float, default=6500.0, help="Initial Teff.") + expert.add_argument("--feh0", type=float, default=-0.5, help="Initial [Fe/H].") + expert.add_argument("--logg0", type=float, default=4.0, help="Initial logg.") + expert.add_argument("--rv0", type=float, default=0.0, help="Initial stellar RV in km/s.") + expert.add_argument( + "--rv-init", + choices=["grid", "none"], + default="grid", + help="RV initialization strategy.", + ) + expert.add_argument( + "--rv-grid-n", + type=int, + default=161, + help="Number of trial RV points in coarse RV scan.", + ) + expert.add_argument("--rv-min", type=float, default=-300.0, help="Minimum RV in km/s.") + expert.add_argument("--rv-max", type=float, default=300.0, help="Maximum RV in km/s.") + expert.add_argument( + "--telluric-threshold", + type=float, + default=0.90, + help="Telluric mask threshold.", + ) + + legacy.add_argument( + "--legacy-centers", + nargs="*", + type=float, + default=None, + help=( + "Line centers in Angstrom for --mode legacy_max. " + "Default: 6495 6545 6561 8498 8542 8662." + ), + ) + legacy.add_argument( + "--legacy-halfwidth", + type=float, + default=10.0, + help="Half-width in Angstrom for legacy windows.", + ) + legacy.add_argument( + "--legacy-flux-min", + type=float, + default=0.2, + help="Minimum normalized flux retained in legacy mode.", + ) + legacy.add_argument( + "--legacy-flux-max", + type=float, + default=1.1, + help="Maximum normalized flux retained in legacy mode.", + ) + legacy.add_argument( + "--legacy-log-scale-min", + type=float, + default=-2.0, + help="Minimum log10 error-scale in legacy mode.", + ) + legacy.add_argument( + "--legacy-log-scale-max", + type=float, + default=2.0, + help="Maximum log10 error-scale in legacy mode.", + ) + legacy.add_argument( + "--legacy-maxiter", + type=int, + default=120, + help="Maximum optimizer iterations in legacy mode.", + ) + + cache.add_argument("--cache-path", default=None, help="Interpolator cache path.") + cache.add_argument("--verbose", type=int, default=1, help="Verbosity level.") + + raw_argv = sys.argv[1:] + args = parser.parse_args() + args = apply_named_preset(args, raw_argv) + + if args.fast: + args.teff_min = 5000.0 + args.teff_max = 6000.0 + args.feh_min = -1.5 + args.feh_max = 0.5 + args.logg_min = 2.5 + args.logg_max = 4.0 + + if args.rv0 != 0.0: + args.rv_min = max(float(args.rv_min), float(args.rv0) - 75.0) + args.rv_max = min(float(args.rv_max), float(args.rv0) + 75.0) + + args.rv_grid_n = min(int(args.rv_grid_n), 41) + args.legacy_maxiter = min(int(args.legacy_maxiter), 50) + args.legacy_log_scale_max = min(float(args.legacy_log_scale_max), 1.0) + args.verbose = min(int(args.verbose), 1) + + config = load_user_config() + phoenix_dir_cfg = get_config_value(config, "paths", "phoenix_dir", default=None) + + args.phoenix_dir = resolve_setting( + args.phoenix_dir, + env_var_name="SPYCTRES_PHOENIX_DIR", + config_value=phoenix_dir_cfg, + default=None, + ) + + if args.mode == "legacy_max": + run_legacy_pepsi_fit(args, parser) + return + + if len(args.files) != 1: + parser.error("quicklook mode accepts exactly one PEPSI file. Use --mode legacy_max for joint fits.") + + args.file = args.files[0] + + if not os.path.isfile(args.file): + parser.error("Input file not found: {0}".format(args.file)) + + if args.phoenix_dir is None: + parser.error( + "No PHOENIX directory supplied. Set --phoenix-dir, SPYCTRES_PHOENIX_DIR, " + "or [paths].phoenix_dir in ~/.config/spyctres/config.toml." + ) + + if not os.path.isdir(args.phoenix_dir): + parser.error("PHOENIX directory not found: {0}".format(args.phoenix_dir)) + + if args.forward_model == "interp_observed" and args.wave_hypothesis == "unknown": + parser.error( + "--forward-model interp_observed requires a known wavelength medium. " + "Use --wave-hypothesis air, vacuum, or air_to_vac, or use native_interp." + ) + + seg0 = read_spectrum(args.file, instrument="pepsi") + seg0 = seg0.copy(mask=build_pepsi_normalized_mask(seg0)) + seg = apply_pepsi_wave_hypothesis(seg0, args.wave_hypothesis) + + if args.window is not None: + window_defs = parse_custom_windows(args.window) + window_preset_used = "custom" + else: + if args.window_preset == "auto": + window_preset_used = choose_auto_window_preset(seg) + else: + window_preset_used = args.window_preset + window_defs = WINDOW_PRESETS[window_preset_used] + + segments = build_window_segments(seg, window_defs, pad=args.window_pad) + + exclude_mask = None + if args.use_telluric_mask: + _, telluric_mask = Spyctres.load_telluric_lines(args.telluric_threshold) + + def exclude_mask(wave): + return np.asarray(telluric_mask(wave)) > 0.5 + + used_masks_plot = [ + build_effective_fit_mask(seg_i, exclude_mask=exclude_mask) + for seg_i in segments + ] + if not any(np.any(m) for m in used_masks_plot): + raise ValueError("No usable points remain after masking.") + + phoenix_lib = PhoenixLibrary(args.phoenix_dir, verbose=bool(args.verbose)) + + teff_avail, feh_avail, logg_avail = phoenix_lib.available_axes() + teff_grid_req = pick_grid_range(teff_avail, args.teff_min, args.teff_max) + feh_grid_req = pick_grid_range(feh_avail, args.feh_min, args.feh_max) + logg_grid_req = pick_grid_range(logg_avail, args.logg_min, args.logg_max) + teff_grid_fit, feh_grid_fit, logg_grid_fit = phoenix_lib.complete_subgrid( + teff_grid_req, feh_grid_req, logg_grid_req + ) + + rv_bary_kms = 0.0 + ssbvel_mps = seg.meta.get("ssbvel_mps") + if args.use_ssbvel and ssbvel_mps is not None: + rv_bary_kms = 1.0e-3 * float(ssbvel_mps) + + R = args.R_override if args.R_override is not None else seg.meta.get("resolution_R", None) + + if args.cache_path is None: + tag = os.path.basename(args.file).replace(".", "_") + args.cache_path = "/tmp/spyctres_{0}_{1}_cache.npz".format(tag, args.wave_hypothesis) + + out = fit_phoenix_full_spectrum( + segments, + phoenix_lib=phoenix_lib, + p0=(args.teff0, args.feh0, args.logg0, args.rv0), + exclude_mask=exclude_mask, + mdeg=args.mdeg, + rv_bary_kms=rv_bary_kms, + R=R, + forward_model=args.forward_model, + model_margin_A=args.model_margin, + teff_grid=teff_grid_fit, + feh_grid=feh_grid_fit, + logg_grid=logg_grid_fit, + cache_path=args.cache_path, + rv_init=None if args.rv_init == "none" else "grid", + rv_grid_n=args.rv_grid_n, + verbose=args.verbose, + max_nfev=300, + ) + + model_list, coeffs_list, used_masks, excluded_masks = reconstruct_phoenix_legendre_models_for_segments( + segments=segments, + phoenix_lib=phoenix_lib, + fit_result=out, + exclude_mask=exclude_mask, + mdeg=args.mdeg, + rv_bary_kms=rv_bary_kms, + R=R, + fwhm_kms=None, + forward_model=args.forward_model, + model_margin_A=args.model_margin, + ) + + print("File:", args.file) + print("Object:", seg.meta.get("object")) + print("Instrument:", seg.meta.get("instrument")) + print("Fiber:", seg.meta.get("fiber")) + print("Cross disperser:", seg.meta.get("cross_disperser")) + print("Window preset used:", window_preset_used) + print("Wave medium hypothesis:", args.wave_hypothesis) + print("Wave medium used:", seg.wave_medium) + print("Wave frame:", seg.wave_frame) + print("SSBVEL m/s:", ssbvel_mps) + print("Barycorr used [km/s]:", rv_bary_kms) + print("Telluric mask:", bool(args.use_telluric_mask)) + print("R used:", R) + print("Teff grid used:", teff_grid_fit) + print("FeH grid used:", feh_grid_fit) + print("logg grid used:", logg_grid_fit) + print("Windows:") + for label, wmin, wmax in window_defs: + print(" ", label, (wmin, wmax)) + print("Best-fit:") + print(" Teff =", out["teff"]) + print(" [Fe/H] =", out["feh"]) + print(" logg =", out["logg"]) + print(" RV =", out["rv_kms"]) + print(" chi2 =", out["chi2"]) + print(" dof =", out["dof"]) + print(" chi2_red =", out["chi2_red"]) + print(" success =", out["success"]) + print(" message =", out["message"]) + print("Continuum coeffs per window:") + for seg_i, coeffs_i in zip(segments, coeffs_list): + print(" ", seg_i.name, coeffs_i) + + wave_plot = concat_with_gap([seg_i.wave for seg_i in segments], gap_value=np.nan, dtype=float) + flux_plot = concat_with_gap([seg_i.flux for seg_i in segments], gap_value=np.nan, dtype=float) + err_plot = concat_with_gap([seg_i.err for seg_i in segments], gap_value=np.nan, dtype=float) + model_plot = concat_with_gap(model_list, gap_value=np.nan, dtype=float) + used_plot = concat_bool_with_gap(used_masks) + excl_plot = concat_bool_with_gap(excluded_masks) + + title = ( + "{0} {1} Teff={2:.0f} [Fe/H]={3:.2f} " + "logg={4:.2f} RV={5:.1f} chi2_red={6:.2f}".format( + os.path.basename(args.file), + args.wave_hypothesis, + out["teff"], + out["feh"], + out["logg"], + out["rv_kms"], + out["chi2_red"], + ) + ) + + fig, axes = plot_full_spectrum_fit( + wave=wave_plot, + flux=flux_plot, + err=err_plot, + model=model_plot, + used_mask=used_plot, + excluded_mask=excl_plot, + title=title, + line_groups=None, + ) + plt.show() + + +if __name__ == "__main__": + main() diff --git a/scripts/phoenix_smoketest.py b/scripts/phoenix_smoketest.py new file mode 100644 index 0000000..32f6c41 --- /dev/null +++ b/scripts/phoenix_smoketest.py @@ -0,0 +1,188 @@ +import os +import argparse +import warnings +import numpy as np + +# pysynphot is legacy and emits a pkg_resources deprecation warning. +# Suppress it in smoke-test scripts to keep output readable. +warnings.filterwarnings( + "ignore", + message=r"pkg_resources is deprecated as an API.*", + category=UserWarning, + module=r"pysynphot.*", +) + +from Spyctres.phoenix import PhoenixLibrary +from Spyctres.config import load_user_config, get_config_value, resolve_setting + +def build_parser(): + return argparse.ArgumentParser( + description=( + "PHOENIX backend smoke test.\n" + "This checks that Spyctres can load a local PHOENIX template, build a " + "small interpolator on a tiny parameter grid, and round-trip that " + "interpolator through the .npz cache." + ), + epilog=( + "Examples:\n" + " export SPYCTRES_PHOENIX_DIR=/path/to/PHOENIXv2\n" + " python scripts/phoenix_smoketest.py\n\n" + " python scripts/phoenix_smoketest.py \\\n" + " --phoenix-dir /path/to/PHOENIXv2 \\\n" + " --cache-path /tmp/spyctres_phoenix_cache_test.npz \\\n" + " --verbose\n" + " ~/.config/spyctres/config.toml:\n" + " [paths]\n" + " phoenix_dir = \"/path/to/PHOENIXv2\"\n" + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + +def main(): + parser = build_parser() + parser.add_argument( + "--phoenix-dir", + default=None, + help="Path to local PHOENIXv2 directory. Precedence: CLI > SPYCTRES_PHOENIX_DIR > config file.", + ) + parser.add_argument( + "--wave-min", + type=float, + default=6500.0, + help="Minimum wavelength in Angstrom for the small test chunk.", + ) + parser.add_argument( + "--wave-max", + type=float, + default=6600.0, + help="Maximum wavelength in Angstrom for the small test chunk.", + ) + parser.add_argument( + "--stride", + type=int, + default=5, + help="Subsampling stride applied to the PHOENIX wavelength grid.", + ) + parser.add_argument( + "--cache-path", + default="/tmp/spyctres_phoenix_cache_test.npz", + help="Cache path used for the interpolator cache round-trip test.", + ) + parser.add_argument( + "--verbose", + action="store_true", + help="Enable verbose PHOENIX library output.", + ) + args = parser.parse_args() + config = load_user_config() + phoenix_dir_cfg = get_config_value(config, "paths", "phoenix_dir", default=None) + + args.phoenix_dir = resolve_setting( + args.phoenix_dir, + env_var_name="SPYCTRES_PHOENIX_DIR", + config_value=phoenix_dir_cfg, + default=None, + ) + if args.phoenix_dir is None: + parser.error( + "No PHOENIX directory supplied. Set --phoenix-dir, SPYCTRES_PHOENIX_DIR, " + "or [paths].phoenix_dir in ~/.config/spyctres/config.toml." + ) + + if not os.path.isdir(args.phoenix_dir): + parser.error("PHOENIX directory not found: {0}".format(args.phoenix_dir)) + + if args.wave_max <= args.wave_min: + parser.error("--wave-max must be greater than --wave-min.") + + if args.stride < 1: + parser.error("--stride must be >= 1.") + + lib = PhoenixLibrary(args.phoenix_dir, verbose=args.verbose) + + # Pick a small wavelength chunk from the PHOENIX wave grid to keep things fast. + # PHOENIX native wavelengths are vacuum wavelengths. + w = lib.phoenix_wave + mask = (w > args.wave_min) & (w < args.wave_max) + wave_small = w[mask][::args.stride] + + if wave_small.size < 10: + parser.error( + "Selected wavelength chunk is too small after masking/stride. " + "Adjust --wave-min/--wave-max/--stride." + ) + + # First check: can we load a single template and resample? + # Users can adjust these to a combination they know exists in their local PHOENIX install. + teff0, logg0, feh0 = 5000, 4.0, 0.0 + wave_out, flux_out = lib.load_template(teff0, logg0, feh0, wave=wave_small) + + print( + "Loaded template:", + teff0, logg0, feh0, + "shape=", flux_out.shape, + "finite=", np.isfinite(flux_out).all(), + ) + + # Second check: build a tiny interpolator on 2x2x2 = 8 templates. + # If any are missing locally, this will raise with a file path that tells you what was not found. + teff_grid = np.array([5000, 5100], dtype=float) + feh_grid = np.array([-0.5, 0.0], dtype=float) + logg_grid = np.array([4.0, 4.5], dtype=float) + + lib.build_interpolator( + observed_wave=wave_small, + teff_grid=teff_grid, + feh_grid=feh_grid, + logg_grid=logg_grid, + cache_path=None, + allow_missing=False, + ) + + f_mid = lib.evaluate(5050, -0.25, 4.25) + print( + "Interpolated spectrum shape=", f_mid.shape, + "finite=", np.isfinite(f_mid).all(), + "min/max=", float(np.min(f_mid)), float(np.max(f_mid)), + ) + + cache_path = args.cache_path + try: + os.remove(cache_path) + except OSError: + pass + + lib.build_interpolator( + observed_wave=wave_small, + teff_grid=teff_grid, + feh_grid=feh_grid, + logg_grid=logg_grid, + cache_path=cache_path, + allow_missing=False, + ) + f1 = lib.evaluate(5050, -0.25, 4.25) + + lib2 = PhoenixLibrary(args.phoenix_dir, verbose=args.verbose) + lib2.build_interpolator( + observed_wave=wave_small, + teff_grid=teff_grid, + feh_grid=feh_grid, + logg_grid=logg_grid, + cache_path=cache_path, + allow_missing=False, + ) + f2 = lib2.evaluate(5050, -0.25, 4.25) + + diff = f1 - f2 + max_abs = float(np.max(np.abs(diff))) + denom = float(np.max(np.abs(f1))) + max_rel = float(max_abs / denom) if denom > 0 else 0.0 + + print("Max |Δ|:", max_abs) + print("Max rel Δ:", max_rel) + print("Cache allclose:", np.allclose(f1, f2, rtol=1e-6, atol=0.0)) + + +if __name__ == "__main__": + main() diff --git a/scripts/xshooter_fit_smoketest.py b/scripts/xshooter_fit_smoketest.py new file mode 100644 index 0000000..bde91df --- /dev/null +++ b/scripts/xshooter_fit_smoketest.py @@ -0,0 +1,1000 @@ +import os +import sys +import argparse + +import warnings +# pysynphot is legacy and emits a pkg_resources deprecation warning. +# Suppress it in smoke-test scripts to keep output readable. +warnings.filterwarnings( + "ignore", + message=r"pkg_resources is deprecated as an API.*", + category=UserWarning, + module=r"pysynphot.*", +) + +import numpy as np +import matplotlib.pyplot as plt + +from Spyctres.io import read_spectrum, SpectrumSegment, make_padded_window_segments +from Spyctres.phoenix import PhoenixLibrary +from Spyctres.waveutils import convert_wavelength_medium +from Spyctres.fitting import ( + fit_phoenix_full_spectrum, + build_effective_fit_mask, +) +from Spyctres.plotting import plot_full_spectrum_fit +from Spyctres.Spyctres import load_telluric_lines +from Spyctres.config import load_user_config, get_config_value, resolve_setting +from Spyctres.recipes import ( + xshooter_balmer_windows, + attach_balmer_metadata, + normalize_segments_sidebands, + make_balmer_core_exclude_mask, + fit_phoenix_sideband_symmetric, + build_plot_models_for_segments, + pick_grid_range, +) + + +def _durbin_watson(x): + x = np.asarray(x, dtype=float) + if x.size < 2: + return np.nan + denom = np.sum(x * x) + if denom <= 0: + return np.nan + return np.sum(np.diff(x) ** 2) / denom + + +def _grid_step(grid): + grid = np.unique(np.asarray(grid, dtype=float)) + if grid.size < 2: + return np.nan + d = np.diff(grid) + d = d[np.isfinite(d) & (d > 0)] + if d.size == 0: + return np.nan + return float(np.min(d)) + + +def _edge_distance_in_steps(value, grid): + grid = np.unique(np.asarray(grid, dtype=float)) + if grid.size == 0: + return np.nan + + step = _grid_step(grid) + dmin = abs(float(value) - float(np.min(grid))) + dmax = abs(float(value) - float(np.max(grid))) + d = min(dmin, dmax) + + if not np.isfinite(step) or step <= 0: + return 0.0 if d == 0.0 else np.nan + + return d / step + + +def _is_on_grid_edge(value, grid, max_edge_distance_steps=0.25): + dsteps = _edge_distance_in_steps(value, grid) + return np.isfinite(dsteps) and (dsteps <= float(max_edge_distance_steps)) + + +def _window_rms(wave, z, center, halfwidth=12.0): + m = np.abs(wave - center) <= float(halfwidth) + if np.sum(m) < 5: + return np.nan + return float(np.sqrt(np.mean(z[m] ** 2))) + + +def _window_annulus_rms(wave, z, center, inner_halfwidth=12.0, outer_halfwidth=30.0): + d = np.abs(wave - center) + m = (d >= float(inner_halfwidth)) & (d <= float(outer_halfwidth)) + if np.sum(m) < 5: + return np.nan + return float(np.sqrt(np.mean(z[m] ** 2))) + + +def _dedupe_p0_list(starts): + out = [] + seen = set() + for s in starts: + s = tuple(float(x) for x in s) + key = tuple(round(x, 6) for x in s) + if key not in seen: + seen.add(key) + out.append(s) + return out + + +def _clamp_start_to_grids(p0, teff_grid=None, feh_grid=None, logg_grid=None): + teff, feh, logg, rv = map(float, p0) + + if teff_grid is not None and len(teff_grid) > 0: + teff = float(np.clip(teff, np.min(teff_grid), np.max(teff_grid))) + if feh_grid is not None and len(feh_grid) > 0: + feh = float(np.clip(feh, np.min(feh_grid), np.max(feh_grid))) + if logg_grid is not None and len(logg_grid) > 0: + logg = float(np.clip(logg, np.min(logg_grid), np.max(logg_grid))) + + return (teff, feh, logg, rv) + + +def build_start_list(args, teff_grid=None, feh_grid=None, logg_grid=None): + starts = [(args.teff0, args.feh0, args.logg0, args.rv0)] + + if args.multistart: + if args.balmer_only: + starts.extend([ + (9000.0, -1.0, 4.0, args.rv0), + (9800.0, -1.0, 3.5, args.rv0), + (10600.0, -1.0, 3.0, args.rv0), + (11600.0, -1.0, 3.0, args.rv0), + (11600.0, -1.5, 3.5, args.rv0), + ]) + else: + starts.extend([ + (4500.0, -0.5, 4.5, args.rv0), + (6500.0, -0.5, 4.0, args.rv0), + (8500.0, -1.0, 3.5, args.rv0), + ]) + + starts = [ + _clamp_start_to_grids(s, teff_grid=teff_grid, feh_grid=feh_grid, logg_grid=logg_grid) + for s in starts + ] + return _dedupe_p0_list(starts) + + +def _flag_present(argv, dest): + """ + Return True if the corresponding CLI flag was explicitly given. + + This lets preset values behave like defaults: explicit CLI flags win. + """ + flag = "--" + str(dest).replace("_", "-") + return any(arg == flag or arg.startswith(flag + "=") for arg in argv) + + +def apply_named_preset(args, argv): + """ + Apply a named benchmark preset, while keeping explicit CLI flags in control. + """ + preset = getattr(args, "preset", None) + if preset is None: + return args + + if preset == "xshooter_uvb_notebook": + preset_values = { + "balmer_only": True, + "norm_mode": "sideband", + "forward_model": "native_interp", + "window_mode": "notebook", + "core_mask": 12.0, + "window_pad": 20.0, + "R_override": 5100.0, + "teff_min": 9500.0, + "teff_max": 12500.0, + "feh_min": -0.5, + "feh_max": 0.5, + "logg_min": 3.0, + "logg_max": 6.0, + "teff0": 9800.0, + "feh0": -0.5, + "logg0": 3.0, + "rv0": -5.0, + "multistart": True, + } + else: + raise ValueError("Unknown preset: {0}".format(preset)) + + for dest, value in preset_values.items(): + if not _flag_present(argv, dest): + setattr(args, dest, value) + + return args + + +def _edge_count(edge_flags): + return int(sum(bool(v) for v in edge_flags.values())) + + +def _installed_axis_limit(value, grid, max_edge_distance_steps=0.25): + """ + Diagnose whether a fitted value is effectively on the minimum or maximum + of the actually installed model axis. + """ + grid = np.unique(np.asarray(grid, dtype=float)) + if grid.size == 0: + return { + "is_edge": False, + "side": None, + "limit_value": np.nan, + "distance_steps": np.nan, + } + + lo = float(np.min(grid)) + hi = float(np.max(grid)) + dsteps = _edge_distance_in_steps(value, grid) + + if not np.isfinite(dsteps) or dsteps > float(max_edge_distance_steps): + return { + "is_edge": False, + "side": None, + "limit_value": np.nan, + "distance_steps": dsteps, + } + + if abs(float(value) - hi) <= abs(float(value) - lo): + side = "upper" + limit_value = hi + else: + side = "lower" + limit_value = lo + + return { + "is_edge": True, + "side": side, + "limit_value": limit_value, + "distance_steps": dsteps, + } + + +def evaluate_installed_limits(result, teff_avail=None, feh_avail=None, logg_avail=None): + out = {} + + if teff_avail is not None: + out["teff"] = _installed_axis_limit(result["teff"], teff_avail) + else: + out["teff"] = None + + if feh_avail is not None: + out["feh"] = _installed_axis_limit(result["feh"], feh_avail) + else: + out["feh"] = None + + if logg_avail is not None: + out["logg"] = _installed_axis_limit(result["logg"], logg_avail) + else: + out["logg"] = None + + summary = [] + if out["teff"] is not None and out["teff"]["is_edge"]: + if out["teff"]["side"] == "upper": + summary.append("Teff >= {0:g} K (installed-grid ceiling)".format(out["teff"]["limit_value"])) + else: + summary.append("Teff <= {0:g} K (installed-grid floor)".format(out["teff"]["limit_value"])) + + if out["feh"] is not None and out["feh"]["is_edge"]: + if out["feh"]["side"] == "upper": + summary.append("[Fe/H] >= {0:g} (installed-grid ceiling)".format(out["feh"]["limit_value"])) + else: + summary.append("[Fe/H] <= {0:g} (installed-grid floor)".format(out["feh"]["limit_value"])) + + if out["logg"] is not None and out["logg"]["is_edge"]: + if out["logg"]["side"] == "upper": + summary.append("logg >= {0:g} (installed-grid ceiling)".format(out["logg"]["limit_value"])) + else: + summary.append("logg <= {0:g} (installed-grid floor)".format(out["logg"]["limit_value"])) + + out["summary"] = summary + return out + + +def select_best_record(records): + return min( + records, + key=lambda rec: ( + float(rec["result"]["chi2_red"]), + _edge_count(rec["quality"]["edge_flags"]), + abs(float(rec["quality"]["z_median"])), + ), + ) + + +def evaluate_fit_quality(seg, model_corr_full, used_mask, result, phoenix_lib): + wave = np.asarray(seg.wave, dtype=float) + flux = np.asarray(seg.flux, dtype=float) + err = np.asarray(seg.err, dtype=float) + used_mask = np.asarray(used_mask, dtype=bool) + + w = wave[used_mask] + f = flux[used_mask] + e = err[used_mask] + m = np.asarray(model_corr_full, dtype=float)[used_mask] + + z = (f - m) / e + + dw = _durbin_watson(z) + z_med = float(np.nanmedian(z)) + z_std = float(np.nanstd(z)) + + # Residual slope in sigma per 1000 Angstrom + xk = (w - np.nanmean(w)) / 1000.0 + if np.sum(np.isfinite(xk) & np.isfinite(z)) >= 2: + slope = float(np.polyfit(xk, z, 1)[0]) + else: + slope = np.nan + + centers_vac = { + "Hdelta": 4101.74, + "Hgamma": 4340.47, + "Hbeta": 4861.33, + } + + wave_medium = str(seg.wave_medium).lower() + balmer_rms = {} + + for label, center_vac in centers_vac.items(): + if wave_medium in ("air", "vacuum"): + center_use = float( + convert_wavelength_medium( + np.array([center_vac], dtype=float), + from_medium="vacuum", + to_medium=wave_medium, + )[0] + ) + else: + center_use = float(center_vac) + + balmer_rms[label] = _window_annulus_rms( + w, z, center_use, + inner_halfwidth=12.0, + outer_halfwidth=30.0, + ) + + tg, zg, gg = phoenix_lib._grid + edge_flags = { + "teff_edge": _is_on_grid_edge(result["teff"], tg), + "feh_edge": _is_on_grid_edge(result["feh"], zg), + "logg_edge": _is_on_grid_edge(result["logg"], gg), + } + + edge_distance_steps = { + "teff_edge_steps": _edge_distance_in_steps(result["teff"], tg), + "feh_edge_steps": _edge_distance_in_steps(result["feh"], zg), + "logg_edge_steps": _edge_distance_in_steps(result["logg"], gg), + } + + verdict = "PASS" + reasons = [] + + # Heuristic thresholds for smoke-test diagnostics + if result["chi2_red"] > 15.0: + verdict = "FAIL" + reasons.append("chi2_red>15") + elif result["chi2_red"] > 5.0: + if verdict != "FAIL": + verdict = "WARN" + reasons.append("chi2_red>5") + + if any(edge_flags.values()): + if verdict != "FAIL": + verdict = "WARN" + reasons.append("parameter_on_grid_edge") + + if np.isfinite(dw) and (dw < 1.2 or dw > 2.8): + if verdict != "FAIL": + verdict = "WARN" + reasons.append("residual_autocorrelation") + + if np.isfinite(slope) and abs(slope) > 1.0: + if verdict != "FAIL": + verdict = "WARN" + reasons.append("residual_slope") + + finite_balmer = [v for v in balmer_rms.values() if np.isfinite(v)] + if len(finite_balmer) > 0 and max(finite_balmer) > 4.0: + if verdict != "FAIL": + verdict = "WARN" + reasons.append("balmer_window_rms>4") + + return { + "verdict": verdict, + "reasons": reasons, + "edge_distance_steps": edge_distance_steps, + "chi2_red": float(result["chi2_red"]), + "dw": float(dw) if np.isfinite(dw) else np.nan, + "z_median": z_med, + "z_std": z_std, + "residual_slope_per_1000A": slope, + "balmer_rms": balmer_rms, + "edge_flags": edge_flags, + } + + +def evaluate_region_quality(segments, model_corr_list, used_masks): + """ + Per-window reduced chi2 diagnostics for Balmer-only mode. + + For each segment, compute reduced chi2 on the actually used pixels. + This is more meaningful than comparing against stale hard-coded + continuum regions from an earlier windowing scheme. + """ + per_window = {} + + labels = ["Hδ", "Hγ", "Hβ"] + for i, (seg, model_corr_full, used_mask) in enumerate(zip(segments, model_corr_list, used_masks)): + wave = np.asarray(seg.wave, dtype=float) + flux = np.asarray(seg.flux, dtype=float) + err = np.asarray(seg.err, dtype=float) + model = np.asarray(model_corr_full, dtype=float) + used_mask = np.asarray(used_mask, dtype=bool) + + m = used_mask & np.isfinite(model) + n = int(np.sum(m)) + + if n <= 4: + chi2_red = np.nan + else: + chi2 = float(np.sum(((flux[m] - model[m]) / err[m]) ** 2)) + chi2_red = chi2 / (n - 4) + + label = labels[i] if i < len(labels) else "win{0}".format(i) + per_window[label] = { + "chi2_red": chi2_red, + "n": n, + "wave_min": float(np.min(wave[m])) if np.any(m) else np.nan, + "wave_max": float(np.max(wave[m])) if np.any(m) else np.nan, + } + + finite_vals = [v["chi2_red"] for v in per_window.values() if np.isfinite(v["chi2_red"])] + + verdict = "PASS" + reasons = [] + if len(finite_vals) > 0 and max(finite_vals) > 5.0: + verdict = "WARN" + reasons.append("line_windows_still_poor") + + return { + "verdict": verdict, + "reasons": reasons, + "per_window": per_window, + } + + +def build_parser(): + return argparse.ArgumentParser( + description=( + "PHOENIX full-spectrum fitting smoke test for reduced X-SHOOTER 1D spectra.\n" + "Use this to exercise the generic package fitter on a real X-SHOOTER file.\n" + "For the validated Balmer-wing benchmark, use --balmer-only --forward-model native_interp " + "--window-mode notebook --core-mask 12." + ), + epilog=( + "Examples:\n" + " export SPYCTRES_PHOENIX_DIR=/path/to/PHOENIXv2\n\n" + " Validated Balmer-wing benchmark:\n" + " python scripts/xshooter_fit_smoketest.py \\\n" + " --balmer-only \\\n" + " --forward-model native_interp \\\n" + " --window-mode notebook \\\n" + " --core-mask 12 \\\n" + " path/to/xshooter_uvb.fits\n\n" + " Sideband-normalized Balmer fit:\n" + " python scripts/xshooter_fit_smoketest.py \\\n" + " --balmer-only \\\n" + " --norm-mode sideband \\\n" + " --forward-model native_interp \\\n" + " --window-mode notebook \\\n" + " --core-mask 12 \\\n" + " path/to/xshooter_uvb.fits\n" + " ~/.config/spyctres/config.toml:\n" + " [paths]\n" + " phoenix_dir = \"/path/to/PHOENIXv2\"\n" + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + +def main(): + parser = build_parser() + parser.add_argument("file", help="Input X-SHOOTER FITS file") + parser.add_argument( + "--phoenix-dir", + default=None, + help="Path to local PHOENIXv2 directory. Precedence: CLI > SPYCTRES_PHOENIX_DIR > config file.", + ) + parser.add_argument( + "--multistart", + action="store_true", + help="Try several starting guesses and keep the best fit", + ) + parser.add_argument( + "--norm-mode", + choices=["poly", "sideband"], + default="poly", + help="Continuum handling for Balmer-only mode: polynomial or sideband normalization", + ) + parser.add_argument( + "--sideband-width", + type=float, + default=10.0, + help="Width in Angstrom of each fallback edge sideband when explicit per-line sidebands are not defined", + ) + parser.add_argument( + "--sideband-order", + type=int, + default=1, + help="Polynomial order for sideband continuum normalization", + ) + parser.add_argument( + "--sideband-poly-order", + type=int, + default=1, + help="Polynomial order for multiplicative wing correction after sideband normalization", + ) + parser.add_argument( + "--R-override", + type=float, + default=None, + help="Override metadata resolving power R for the fit", + ) + parser.add_argument( + "--preset", + choices=["xshooter_uvb_notebook"], + default=None, + help=( + "Apply a named benchmark preset. " + "'xshooter_uvb_notebook' reproduces the notebook-faithful UVB " + "sideband benchmark settings. Explicit CLI flags override preset values." + ), + ) + parser.add_argument( + "--forward-model", + choices=["interp_observed", "native_interp"], + default="interp_observed", + help="Forward-model path. 'native_interp' is the validated wavelength-space reference and the recommended path; 'interp_observed' is the legacy observed-grid path.", + ) + parser.add_argument( + "--model-margin", + type=float, + default=200.0, + help="Margin in Angstrom for native_interp model preparation", + ) + parser.add_argument("--wmin", type=float, default=3980.0, help="Minimum wavelength in Angstrom") + parser.add_argument("--wmax", type=float, default=5500.0, help="Maximum wavelength in Angstrom") + parser.add_argument("--teff-min", type=float, default=9000.0, + help="Minimum Teff for explicit PHOENIX grid in Balmer-only mode") + parser.add_argument("--teff-max", type=float, default=11600.0, + help="Maximum Teff for explicit PHOENIX grid in Balmer-only mode") + parser.add_argument("--feh-min", type=float, default=-2.0, + help="Minimum [Fe/H] for explicit PHOENIX grid in Balmer-only mode") + parser.add_argument("--feh-max", type=float, default=0.0, + help="Maximum [Fe/H] for explicit PHOENIX grid in Balmer-only mode") + parser.add_argument("--logg-min", type=float, default=2.0, + help="Minimum logg for explicit PHOENIX grid in Balmer-only mode") + parser.add_argument("--logg-max", type=float, default=4.5, + help="Maximum logg for explicit PHOENIX grid in Balmer-only mode") + parser.add_argument("--clip-left", type=int, default=0, help="Clip this many pixels from the left edge") + parser.add_argument("--clip-right", type=int, default=0, help="Clip this many pixels from the right edge") + parser.add_argument("--balmer-only", action="store_true", + help="Fit only Balmer windows instead of the full selected wavelength range") + parser.add_argument("--core-mask", type=float, default=10.0, + help="Half-width in Angstrom to mask around Balmer line centers in --balmer-only mode. For X-SHOOTER UVB Balmer-wing classification, 6-12 A is the recommended range to explore.") + parser.add_argument("--window-pad", type=float, default=5.0, + help="Padding in Angstrom added on each side of Balmer fit windows") + parser.add_argument( + "--window-mode", + choices=["current", "notebook"], + default="current", + help="Use the current narrow Balmer windows or the broader notebook-style windows. The validated benchmark uses 'notebook'.", + ) + parser.add_argument("--mdeg", type=int, default=2, help="Legendre continuum degree") + parser.add_argument("--teff0", type=float, default=5000.0, help="Initial Teff") + parser.add_argument("--feh0", type=float, default=-0.5, help="Initial [Fe/H]") + parser.add_argument("--logg0", type=float, default=4.0, help="Initial logg") + parser.add_argument("--rv0", type=float, default=0.0, help="Initial stellar RV in km/s") + parser.add_argument("--rv-init", choices=["grid", "none"], default="grid", help="RV initialization strategy") + parser.add_argument("--rv-grid-n", type=int, default=81, help="Number of trial RV points in coarse RV scan") + parser.add_argument("--telluric-threshold", type=float, default=0.90, help="Telluric mask threshold") + parser.add_argument("--use-telluric-mask", action="store_true", help="Apply built-in telluric mask") + parser.add_argument("--use-barycorr", action="store_true", help="Pass header barycentric correction into fit") + parser.add_argument("--cache-path", default="/tmp/spyctres_xshooter_fit_cache.npz") + parser.add_argument("--verbose", type=int, default=1) + raw_argv = sys.argv[1:] + args = parser.parse_args() + args = apply_named_preset(args, raw_argv) + + config = load_user_config() + phoenix_dir_cfg = get_config_value(config, "paths", "phoenix_dir", default=None) + + args.phoenix_dir = resolve_setting( + args.phoenix_dir, + env_var_name="SPYCTRES_PHOENIX_DIR", + config_value=phoenix_dir_cfg, + default=None, + ) + + if not os.path.isfile(args.file): + parser.error("Input file not found: {0}".format(args.file)) + + if args.norm_mode == "sideband" and not args.balmer_only: + parser.error("--norm-mode sideband currently requires --balmer-only.") + + if args.phoenix_dir is None: + parser.error( + "No PHOENIX directory supplied. Set --phoenix-dir, SPYCTRES_PHOENIX_DIR, " + "or [paths].phoenix_dir in ~/.config/spyctres/config.toml." + ) + + if not os.path.isdir(args.phoenix_dir): + parser.error("PHOENIX directory not found: {0}".format(args.phoenix_dir)) + + if args.wmax <= args.wmin: + parser.error("--wmax must be greater than --wmin.") + + seg0 = read_spectrum(args.file, instrument="xshooter") + arm = str(seg0.meta.get("arm", "")).strip().upper() + if arm and arm != "UVB": + parser.error( + "This script is currently configured for X-SHOOTER UVB fitting, but the reader reported arm={0!r}.".format(arm) + ) + + seg_clip = seg0.window( + wmin=args.wmin, + wmax=args.wmax, + clip_left=args.clip_left, + clip_right=args.clip_right, + name_suffix="fitwin", + ) + + balmer_windows = xshooter_balmer_windows(args.window_mode) + + if args.balmer_only: + segments = make_padded_window_segments( + seg_clip, + [(wmin, wmax) for _, wmin, wmax in balmer_windows], + pad=args.window_pad, + name_prefix="balmer", + ) + + # Rename the generic padded-window segments to the physical Balmer labels + # expected by the recipe metadata helper. + for seg_i, (label, _wmin, _wmax) in zip(segments, balmer_windows): + seg_i.name = label + else: + segments = [seg_clip] + + if args.balmer_only: + if args.window_mode == "notebook": + attach_balmer_metadata(segments) + else: + current_cont_windows = {label: None for label, _, _ in balmer_windows} + attach_balmer_metadata(segments, cont_windows=current_cont_windows) + + norm_info = None + fit_mdeg = args.mdeg + + if args.balmer_only and args.norm_mode == "sideband": + segments, norm_info = normalize_segments_sidebands( + segments, + sideband_width=args.sideband_width, + sideband_order=args.sideband_order, + ) + + seg_ref = segments[0] + exclude_mask_list = [] + + if args.use_telluric_mask: + _, telluric_mask = load_telluric_lines(args.telluric_threshold) + exclude_mask_list.append(telluric_mask) + + if args.balmer_only: + core_mask = make_balmer_core_exclude_mask( + core_halfwidth=args.core_mask, + wave_medium=seg_ref.wave_medium, + ) + exclude_mask_list.append(core_mask) + + if len(exclude_mask_list) == 0: + exclude_mask = None + else: + def exclude_mask(wave): + wave = np.asarray(wave, dtype=float) + m = np.zeros_like(wave, dtype=bool) + for fn in exclude_mask_list: + m |= (np.asarray(fn(wave)) > 0.5) + return m + + used_masks_plot = [ + build_effective_fit_mask(seg, exclude_mask=exclude_mask) + for seg in segments + ] + + if not any(np.any(m) for m in used_masks_plot): + raise ValueError("No usable points remain after masking.") + + phoenix_lib = PhoenixLibrary(args.phoenix_dir, verbose=bool(args.verbose)) + + if args.balmer_only: + teff_avail, feh_avail, logg_avail = phoenix_lib.available_axes() + print("Installed Teff range:", float(np.min(teff_avail)), float(np.max(teff_avail))) + print("Installed FeH range:", float(np.min(feh_avail)), float(np.max(feh_avail))) + print("Installed logg range:", float(np.min(logg_avail)), float(np.max(logg_avail))) + + teff_grid_req = pick_grid_range(teff_avail, args.teff_min, args.teff_max) + feh_grid_req = pick_grid_range(feh_avail, args.feh_min, args.feh_max) + logg_grid_req = pick_grid_range(logg_avail, args.logg_min, args.logg_max) + + teff_grid_fit, feh_grid_fit, logg_grid_fit = phoenix_lib.complete_subgrid( + teff_grid_req, feh_grid_req, logg_grid_req + ) + else: + teff_avail = None + feh_avail = None + logg_avail = None + teff_grid_req = None + feh_grid_req = None + logg_grid_req = None + teff_grid_fit = None + feh_grid_fit = None + logg_grid_fit = None + + start_list = build_start_list( + args, + teff_grid=teff_grid_fit, + feh_grid=feh_grid_fit, + logg_grid=logg_grid_fit, + ) + + rv_bary_kms = 0.0 + if args.use_barycorr: + rv_bary_kms = float(seg_ref.meta.get("barycorr_kms") or 0.0) + + R = args.R_override if args.R_override is not None else seg_ref.meta.get("resolution_R", None) + + records = [] + + for i, p0_i in enumerate(start_list, start=1): + print("\n=== START {0}/{1}: p0={2} ===".format(i, len(start_list), p0_i)) + + if args.norm_mode == "sideband": + result_i = fit_phoenix_sideband_symmetric( + segments=segments, + phoenix_lib=phoenix_lib, + p0=p0_i, + exclude_mask=exclude_mask, + rv_bary_kms=rv_bary_kms, + R=R, + forward_model=args.forward_model, + model_margin_A=args.model_margin, + teff_grid=teff_grid_fit, + feh_grid=feh_grid_fit, + logg_grid=logg_grid_fit, + cache_path=args.cache_path, + rv_init=None if args.rv_init == "none" else "grid", + rv_grid_n=args.rv_grid_n, + verbose=args.verbose, + max_nfev=200, + sideband_width=args.sideband_width, + sideband_order=args.sideband_order, + sideband_poly_order=args.sideband_poly_order, + ) + else: + result_i = fit_phoenix_full_spectrum( + segments=segments, + phoenix_lib=phoenix_lib, + p0=p0_i, + exclude_mask=exclude_mask, + mdeg=fit_mdeg, + rv_bary_kms=rv_bary_kms, + R=R, + forward_model=args.forward_model, + model_margin_A=args.model_margin, + teff_grid=teff_grid_fit, + feh_grid=feh_grid_fit, + logg_grid=logg_grid_fit, + cache_path=args.cache_path, + rv_init=None if args.rv_init == "none" else "grid", + rv_grid_n=args.rv_grid_n, + verbose=args.verbose, + max_nfev=200, + ) + + model_corr_list_i, coeffs_list_i, used_masks_i, excluded_masks_i = build_plot_models_for_segments( + segments=segments, + phoenix_lib=phoenix_lib, + fit_result=result_i, + exclude_mask=exclude_mask, + mdeg=fit_mdeg, + rv_bary_kms=rv_bary_kms, + R=R, + fwhm_kms=None, + norm_mode=args.norm_mode, + sideband_width=args.sideband_width, + sideband_order=args.sideband_order, + sideband_poly_order=args.sideband_poly_order, + forward_model=args.forward_model, + model_margin_A=args.model_margin, + ) + + wave_cat = np.concatenate([np.asarray(s.wave, dtype=float) for s in segments]) + flux_cat = np.concatenate([np.asarray(s.flux, dtype=float) for s in segments]) + err_cat = np.concatenate([np.asarray(s.err, dtype=float) for s in segments]) + model_cat = np.concatenate(model_corr_list_i) + used_cat = np.concatenate(used_masks_i) + + seg_metrics = SpectrumSegment( + wave=wave_cat, + flux=flux_cat, + err=err_cat, + mask=used_cat, + meta=dict(seg_ref.meta), + wave_medium=seg_ref.wave_medium, + wave_frame=seg_ref.wave_frame, + name="metrics_concat", + ) + + quality_i = evaluate_fit_quality( + seg=seg_metrics, + model_corr_full=model_cat, + used_mask=used_cat, + result=result_i, + phoenix_lib=phoenix_lib, + ) + + region_quality_i = evaluate_region_quality( + segments=segments, + model_corr_list=model_corr_list_i, + used_masks=used_masks_i, + ) + + records.append({ + "p0": p0_i, + "result": result_i, + "model_corr_list": model_corr_list_i, + "coeffs_list": coeffs_list_i, + "used_masks": used_masks_i, + "excluded_masks": excluded_masks_i, + "quality": quality_i, + "region_quality": region_quality_i, + }) + + print( + " -> chi2_red={0:.6f} teff={1:.1f} feh={2:.3f} logg={3:.3f} rv={4:.2f} edge_flags={5}".format( + result_i["chi2_red"], + result_i["teff"], + result_i["feh"], + result_i["logg"], + result_i["rv_kms"], + quality_i["edge_flags"], + ) + ) + + best_rec = select_best_record(records) + + selected_p0 = best_rec["p0"] + result = best_rec["result"] + model_corr_list = best_rec["model_corr_list"] + coeffs_list = best_rec["coeffs_list"] + used_masks_plot = best_rec["used_masks"] + excluded_masks_plot = best_rec["excluded_masks"] + quality = best_rec["quality"] + region_quality = best_rec["region_quality"] + installed_limits = evaluate_installed_limits( + result=result, + teff_avail=teff_avail, + feh_avail=feh_avail, + logg_avail=logg_avail, + ) + + print("File:", args.file) + print("Preset:", args.preset) + print("Object:", seg_ref.meta.get("object")) + print("Arm:", seg_ref.meta.get("arm")) + print("Pixels used:", int(sum(np.sum(m) for m in used_masks_plot)), "/", int(sum(len(s.wave) for s in segments))) + print("Window [A]:", (args.wmin, args.wmax)) + print("Telluric mask:", bool(args.use_telluric_mask)) + print("Barycorr used [km/s]:", rv_bary_kms) + print("R used:", R) + print("R override:", args.R_override) + print("Teff grid requested:", teff_grid_req) + print("FeH grid requested:", feh_grid_req) + print("logg grid requested:", logg_grid_req) + print("Teff grid used:", teff_grid_fit) + print("FeH grid used:", feh_grid_fit) + print("logg grid used:", logg_grid_fit) + print("Normalization mode:", args.norm_mode) + + if norm_info is not None: + for label, info in zip([x[0] for x in balmer_windows], norm_info): + print( + " ", label, + "sidebands N =", info["n_sideband"], + "fit=({0:.1f},{1:.1f})".format(info["fit_lo"], info["fit_hi"]), + "mode =", info["mode"], + ) + + if args.norm_mode == "sideband": + print(" sideband_order:", args.sideband_order) + print(" sideband_poly_order:", args.sideband_poly_order) + + print("Best-fit:") + print(" Teff =", result["teff"]) + print(" [Fe/H] =", result["feh"]) + print(" logg =", result["logg"]) + print(" RV =", result["rv_kms"]) + print(" chi2 =", result["chi2"]) + print(" dof =", result["dof"]) + print(" chi2_red =", result["chi2_red"]) + print(" success =", result["success"]) + print(" message =", result["message"]) + print("Continuum coeffs per segment:", coeffs_list) + print("Quality verdict:", quality["verdict"]) + print("Quality reasons:", quality["reasons"]) + print("Region quality verdict:", region_quality["verdict"]) + print("Region quality reasons:", region_quality["reasons"]) + print(" Per-window chi2_red:") + for k, v in region_quality["per_window"].items(): + print(" ", k, "chi2_red =", v["chi2_red"], "N =", v["n"], + "range=({0:.1f},{1:.1f})".format(v["wave_min"], v["wave_max"])) + print("Installed-grid limits:", installed_limits["summary"]) + print(" Durbin-Watson:", quality["dw"]) + print(" Residual median:", quality["z_median"]) + print(" Residual std:", quality["z_std"]) + print(" Residual slope [sigma / 1000 A]:", quality["residual_slope_per_1000A"]) + print(" Edge flags:", quality["edge_flags"]) + print(" Edge distance [grid steps]:", quality["edge_distance_steps"]) + print("Start list tried:", start_list) + print("Selected start:", selected_p0) + print("Window mode:", args.window_mode) + print(" Balmer-window RMS:", quality["balmer_rms"]) + + title = "{0} {1:.0f}-{2:.0f} A Teff={3:.0f} [Fe/H]={4:.2f} logg={5:.2f} RV={6:.1f} chi2_red={7:.2f}".format( + os.path.basename(args.file), + args.wmin, + args.wmax, + result["teff"], + result["feh"], + result["logg"], + result["rv_kms"], + result["chi2_red"], + ) + + if args.balmer_only: + fig, axes = plt.subplots(2, 2, figsize=(12, 8)) + axes = axes.ravel() + + for ax, (label, _, _), seg_i, model_i, excl_i in zip( + axes, balmer_windows, segments, model_corr_list, excluded_masks_plot + ): + ax.plot(seg_i.wave, seg_i.flux, lw=1.0, label="Data") + ax.plot(seg_i.wave, model_i, lw=1.0, label="Model") + + if np.any(excl_i): + y0, y1 = ax.get_ylim() + ax.fill_between( + seg_i.wave, y0, y1, + where=excl_i, + alpha=0.15, + step=None, + ) + ax.set_ylim(y0, y1) + + ax.set_title(label) + ax.set_xlabel("Wavelength (Å)") + ax.set_ylabel("Flux") + ax.legend(loc="best") + + if len(axes) > len(segments): + for ax in axes[len(segments):]: + ax.axis("off") + + fig.suptitle(title) + fig.tight_layout() + plt.show() + else: + fig, axes = plot_full_spectrum_fit( + wave=segments[0].wave, + flux=segments[0].flux, + err=segments[0].err, + model=model_corr_list[0], + used_mask=used_masks_plot[0], + excluded_mask=excluded_masks_plot[0], + title=title, + line_groups=["balmer", "caii", "hei"], + ) + plt.show() + + +if __name__ == "__main__": + main() diff --git a/scripts/xshooter_notebook_scan_smoketest.py b/scripts/xshooter_notebook_scan_smoketest.py new file mode 100644 index 0000000..f1c8153 --- /dev/null +++ b/scripts/xshooter_notebook_scan_smoketest.py @@ -0,0 +1,831 @@ +import os +import argparse +import warnings + +warnings.filterwarnings( + "ignore", + message=r"pkg_resources is deprecated as an API.*", + category=UserWarning, + module=r"pysynphot.*", +) + +import numpy as np +import matplotlib.pyplot as plt + +from Spyctres.io import read_spectrum, SpectrumSegment, make_padded_window_segments +from Spyctres.phoenix import PhoenixLibrary +from Spyctres.waveutils import convert_wavelength_medium +from Spyctres.Spyctres import load_telluric_lines +from Spyctres.config import load_user_config, get_config_value, resolve_setting +from scipy.interpolate import interp1d +from scipy.ndimage import gaussian_filter1d +from Spyctres.recipes import pick_grid_range + +C_KMS = 299792.458 + + +def doppler_shift_wave(wave_A, rv_kms): + wave_A = np.asarray(wave_A, dtype=np.float64) + return wave_A * (1.0 + float(rv_kms) / C_KMS) + + +def convolve_to_resolution_loglam(wave_A, flux, R): + """ + Convolve with a Gaussian in log-lambda, matching the notebook cell-54 path. + """ + wave_A = np.asarray(wave_A, dtype=np.float64) + flux = np.asarray(flux, dtype=np.float64) + + if R is None: + return flux.copy() + + if not np.all(np.diff(wave_A) > 0): + idx = np.argsort(wave_A) + wave_A = wave_A[idx] + flux = flux[idx] + + m = np.isfinite(wave_A) & (wave_A > 0) & np.isfinite(flux) + wave_A = wave_A[m] + flux = flux[m] + + if len(wave_A) < 5: + return flux.copy() + + loglam = np.log(wave_A) + dloglam = np.median(np.diff(loglam)) + + sigma_v = C_KMS / (float(R) * 2.355) + sigma_pix = (sigma_v / C_KMS) / dloglam + + if sigma_pix < 0.3: + return flux.copy() + + return gaussian_filter1d(flux, sigma_pix, mode="nearest") + + +def resample_flux(w_src, f_src, w_tgt): + w_src = np.asarray(w_src, dtype=np.float64) + f_src = np.asarray(f_src, dtype=np.float64) + w_tgt = np.asarray(w_tgt, dtype=np.float64) + + m = np.isfinite(w_src) & np.isfinite(f_src) + w_src = w_src[m] + f_src = f_src[m] + + if len(w_src) < 4: + return np.full_like(w_tgt, np.nan, dtype=float) + + if not np.all(np.diff(w_src) > 0): + idx = np.argsort(w_src) + w_src = w_src[idx] + f_src = f_src[idx] + + f = interp1d( + w_src, + f_src, + kind="linear", + bounds_error=False, + fill_value="extrapolate", + ) + return np.asarray(f(w_tgt), dtype=float) + + +def _build_sideband_mask(seg, wave, fit_mask, sideband_width=10.0): + wave = np.asarray(wave, dtype=float) + fit_mask = np.asarray(fit_mask, dtype=bool) + + cont_windows = seg.meta.get("cont_windows", None) + center = seg.meta.get("line_center_data", None) + + if cont_windows is not None and center is not None: + sb_mask = np.zeros_like(wave, dtype=bool) + center = float(center) + for a, b in cont_windows: + sb_mask |= fit_mask & (wave > center + float(a)) & (wave < center + float(b)) + return sb_mask + + fit_wave = wave[fit_mask] + lo = float(np.min(fit_wave)) + hi = float(np.max(fit_wave)) + sb_mask = fit_mask & ( + ((wave >= lo) & (wave <= lo + float(sideband_width))) | + ((wave >= hi - float(sideband_width)) & (wave <= hi)) + ) + return sb_mask + + +def normalize_segment_sidebands(seg, sideband_width=10.0, sideband_order=1): + wave = np.asarray(seg.wave, dtype=float) + flux = np.asarray(seg.flux, dtype=float) + err = None if seg.err is None else np.asarray(seg.err, dtype=float) + fit_mask = np.asarray(seg.mask, dtype=bool) + + if np.sum(fit_mask) < 6: + return seg + + sb_mask = _build_sideband_mask(seg, wave, fit_mask, sideband_width=sideband_width) + + good = sb_mask & np.isfinite(wave) & np.isfinite(flux) + if err is not None: + good &= np.isfinite(err) & (err > 0) + + order = int(sideband_order) + + if np.sum(good) >= (order + 2): + if err is None: + coeffs = np.polyfit(wave[good], flux[good], deg=order) + else: + coeffs = np.polyfit(wave[good], flux[good], deg=order, w=1.0 / err[good]) + cont = np.polyval(coeffs, wave) + else: + level = float(np.nanmedian(flux[fit_mask])) + cont = np.full_like(wave, level, dtype=float) + + pos = np.isfinite(cont) & (cont > 0) + fallback = float(np.nanmedian(cont[pos])) + cont = np.where(np.isfinite(cont) & (cont > 0), cont, fallback) + + flux_n = flux / cont + err_n = None if err is None else err / cont + + seg_n = SpectrumSegment( + wave=wave, + flux=flux_n, + err=err_n, + mask=fit_mask, + meta=dict(seg.meta), + wave_medium=seg.wave_medium, + wave_frame=seg.wave_frame, + name=seg.name, + ) + return seg_n + + +def normalize_segments_sidebands(segments, sideband_width=10.0, sideband_order=1): + return [ + normalize_segment_sidebands( + seg, + sideband_width=sideband_width, + sideband_order=sideband_order, + ) + for seg in segments + ] + + +def normalize_model_sidebands(seg, model_flux, sideband_width=10.0, sideband_order=1): + wave = np.asarray(seg.wave, dtype=float) + model_flux = np.asarray(model_flux, dtype=float) + fit_mask = np.asarray(seg.mask, dtype=bool) + + if np.sum(fit_mask) < 6: + return model_flux.copy() + + sb_mask = _build_sideband_mask(seg, wave, fit_mask, sideband_width=sideband_width) + good = sb_mask & np.isfinite(wave) & np.isfinite(model_flux) + order = int(sideband_order) + + if np.sum(good) >= (order + 2): + coeffs = np.polyfit(wave[good], model_flux[good], deg=order) + cont = np.polyval(coeffs, wave) + else: + level = float(np.nanmedian(model_flux[fit_mask])) + cont = np.full_like(wave, level, dtype=float) + + pos = np.isfinite(cont) & (cont > 0) + fallback = float(np.nanmedian(cont[pos])) + cont = np.where(np.isfinite(cont) & (cont > 0), cont, fallback) + + return model_flux / cont + + +def _solve_sideband_multiplicative_poly(wave, flux, err, model, used_mask, order=1): + wave = np.asarray(wave, dtype=float) + flux = np.asarray(flux, dtype=float) + err = np.asarray(err, dtype=float) + model = np.asarray(model, dtype=float) + used_mask = np.asarray(used_mask, dtype=bool) + order = int(order) + + if order <= 0: + return model.copy(), np.array([1.0], dtype=float) + + good = ( + used_mask & + np.isfinite(wave) & + np.isfinite(flux) & + np.isfinite(err) & (err > 0) & + np.isfinite(model) + ) + + if np.sum(good) < (order + 2): + return model.copy(), np.array([1.0], dtype=float) + + x0 = float(np.mean(wave[good])) + xscale = float(np.ptp(wave[good])) + if (not np.isfinite(xscale)) or (xscale <= 0): + xscale = 1.0 + + x = (wave[good] - x0) / xscale + A = np.vander(x, N=order + 1, increasing=True) + + rhs = flux[good] / (model[good] + 1e-30) + W = 1.0 / err[good] + + Aw = A * W[:, None] + bw = rhs * W + coeffs, *_ = np.linalg.lstsq(Aw, bw, rcond=None) + + x_all = (wave - x0) / xscale + poly_all = np.vander(x_all, N=order + 1, increasing=True) @ coeffs + + return model * poly_all, coeffs + + +def make_balmer_core_exclude_mask(core_halfwidth=3.0, wave_medium="vacuum"): + centers_vac = np.array([4101.74, 4340.47, 4861.33], dtype=float) + + wave_medium = str(wave_medium).lower() + if wave_medium in ("air", "vacuum"): + centers = convert_wavelength_medium( + centers_vac, + from_medium="vacuum", + to_medium=wave_medium, + ) + else: + centers = centers_vac.copy() + + def _mask(wave): + wave = np.asarray(wave, dtype=float) + m = np.zeros_like(wave, dtype=bool) + for c in centers: + m |= np.abs(wave - c) <= float(core_halfwidth) + return m + + return _mask + + +def build_used_mask(seg, exclude_mask=None): + m = np.asarray(seg.mask, dtype=bool) + m &= np.isfinite(seg.wave) & np.isfinite(seg.flux) + + if seg.err is not None: + m &= np.isfinite(seg.err) & (seg.err > 0) + + if exclude_mask is not None: + m &= ~(np.asarray(exclude_mask(seg.wave)) > 0.5) + + return m + + +def build_excluded_mask(seg, exclude_mask=None): + m = np.zeros_like(seg.wave, dtype=bool) + if exclude_mask is not None: + m |= (np.asarray(exclude_mask(seg.wave)) > 0.5) + return m + + +def evaluate_template_on_segments( + segments, + phoenix_wave_native, + template_flux_native, + rv_kms, + rv_bary_kms, + R, + exclude_mask, + sideband_width, + sideband_order, + sideband_poly_order, + phoenix_wave_medium="vacuum", + model_margin_A=200.0, + return_models=False, +): + """ + Notebook-faithful template evaluation: + + 1. Convert native PHOENIX wavelengths into the data wavelength convention. + 2. Subset a wide region before any model operations. + 3. Doppler shift on the native/high-res grid. + 4. Convolve in log-lambda at resolving power R. + 5. Resample to each observed segment grid. + 6. Apply the same sideband normalization and wing polynomial as the data path. + """ + used_masks = [build_used_mask(seg, exclude_mask=exclude_mask) for seg in segments] + excluded_masks = [build_excluded_mask(seg, exclude_mask=exclude_mask) for seg in segments] + + fit_los = [] + fit_his = [] + for seg in segments: + m_fit = np.asarray(seg.mask, dtype=bool) + if np.any(m_fit): + fit_los.append(float(np.min(np.asarray(seg.wave, dtype=float)[m_fit]))) + fit_his.append(float(np.max(np.asarray(seg.wave, dtype=float)[m_fit]))) + + if len(fit_los) == 0: + raise ValueError("No fit pixels found in segments.") + + wmin_need = min(fit_los) - float(model_margin_A) + wmax_need = max(fit_his) + float(model_margin_A) + + segment_media = sorted(set(str(seg.wave_medium).lower() for seg in segments)) + if len(segment_media) == 1 and segment_media[0] in ("air", "vacuum"): + data_wave_medium = segment_media[0] + else: + data_wave_medium = str(phoenix_wave_medium).lower() + + if data_wave_medium != str(phoenix_wave_medium).lower(): + w_ph_all = convert_wavelength_medium( + np.asarray(phoenix_wave_native, dtype=float), + from_medium=str(phoenix_wave_medium).lower(), + to_medium=data_wave_medium, + ) + else: + w_ph_all = np.asarray(phoenix_wave_native, dtype=float).copy() + + f_ph_all = np.asarray(template_flux_native, dtype=float) + + m_ph = ( + np.isfinite(w_ph_all) & + np.isfinite(f_ph_all) & + (w_ph_all > 0) & + (w_ph_all >= wmin_need) & + (w_ph_all <= wmax_need) + ) + + w_ph = w_ph_all[m_ph] + f_ph = f_ph_all[m_ph] + + if len(w_ph) < 10: + raise ValueError("Too few PHOENIX points remain after notebook-style subsetting.") + + rv_tot = float(rv_bary_kms) + float(rv_kms) + w_shift = doppler_shift_wave(w_ph, rv_tot) + + m_ok = np.isfinite(w_shift) & (w_shift > 0) & np.isfinite(f_ph) + w_shift = w_shift[m_ok] + f_ph = f_ph[m_ok] + + f_conv = convolve_to_resolution_loglam(w_shift, f_ph, R) + + chi2 = 0.0 + npts = 0 + model_corr_list = [] + coeffs_list = [] + per_window = {} + + for i, (seg, used_mask, excl_mask) in enumerate(zip(segments, used_masks, excluded_masks)): + wave = np.asarray(seg.wave, dtype=float) + flux = np.asarray(seg.flux, dtype=float) + err = np.asarray(seg.err, dtype=float) + used_mask = np.asarray(used_mask, dtype=bool) + excl_mask = np.asarray(excl_mask, dtype=bool) + + idx = np.argsort(wave) + wave = wave[idx] + flux = flux[idx] + err = err[idx] + used_mask = used_mask[idx] + excl_mask = excl_mask[idx] + + model0 = resample_flux(w_shift, f_conv, wave) + model_norm = normalize_model_sidebands( + seg=SpectrumSegment( + wave=wave, + flux=flux, + err=err, + mask=np.asarray(seg.mask, dtype=bool)[idx], + meta=dict(seg.meta), + wave_medium=seg.wave_medium, + wave_frame=seg.wave_frame, + name=seg.name, + ), + model_flux=model0, + sideband_width=sideband_width, + sideband_order=sideband_order, + ) + + model_corr, coeffs = _solve_sideband_multiplicative_poly( + wave=wave, + flux=flux, + err=err, + model=model_norm, + used_mask=used_mask, + order=sideband_poly_order, + ) + + z = (flux[used_mask] - model_corr[used_mask]) / err[used_mask] + chi2_i = float(np.sum(z * z)) + n_i = int(np.sum(used_mask)) + + chi2 += chi2_i + npts += n_i + model_corr_list.append(model_corr) + coeffs_list.append(coeffs) + + label = str(seg.meta.get("line_label", f"win{i}")) + per_window[label] = { + "chi2_red": np.nan if n_i <= 4 else chi2_i / (n_i - 4), + "n": n_i, + "wave_min": float(np.min(wave[used_mask])) if np.any(used_mask) else np.nan, + "wave_max": float(np.max(wave[used_mask])) if np.any(used_mask) else np.nan, + } + + excluded_masks[i] = excl_mask + + out = { + "chi2": chi2, + "npts": npts, + "chi2_red": np.nan if npts <= 4 else chi2 / (npts - 4), + "used_masks": used_masks, + "excluded_masks": excluded_masks, + "per_window": per_window, + } + + if return_models: + out["model_corr_list"] = model_corr_list + out["coeffs_list"] = coeffs_list + + return out + +def build_parser(): + return argparse.ArgumentParser( + description=( + "Notebook-faithful discrete PHOENIX grid scan for X-SHOOTER UVB Balmer windows.\n" + "This is a validation reference script, not the generic package fitter.\n" + "It expects a reduced X-SHOOTER UVB FITS file and a local PHOENIXv2 installation." + ), + epilog=( + "Examples:\n" + " export SPYCTRES_PHOENIX_DIR=/path/to/PHOENIXv2\n" + " python scripts/xshooter_notebook_scan_smoketest.py path/to/xshooter_uvb.fits\n\n" + " python scripts/xshooter_notebook_scan_smoketest.py \\\n" + " --phoenix-dir /path/to/PHOENIXv2 \\\n" + " --use-telluric-mask --use-barycorr \\\n" + " path/to/xshooter_uvb.fits\n" + " ~/.config/spyctres/config.toml:\n" + " [paths]\n" + " phoenix_dir = \"/path/to/PHOENIXv2\"\n" + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + +def main(): + parser = build_parser() + parser.add_argument("file", help="Input X-SHOOTER UVB FITS file") + parser.add_argument( + "--phoenix-dir", + default=None, + help="Path to local PHOENIXv2 directory. Precedence: CLI > SPYCTRES_PHOENIX_DIR > config file.", + ) + parser.add_argument("--wmin", type=float, default=3980.0) + parser.add_argument("--wmax", type=float, default=5500.0) + parser.add_argument("--clip-left", type=int, default=0) + parser.add_argument("--clip-right", type=int, default=0) + parser.add_argument("--core-mask", type=float, default=12.0) + parser.add_argument("--window-pad", type=float, default=20.0) + parser.add_argument("--sideband-width", type=float, default=10.0) + parser.add_argument("--sideband-order", type=int, default=1) + parser.add_argument("--sideband-poly-order", type=int, default=1) + parser.add_argument("--R-override", type=float, default=5100.0) + parser.add_argument( + "--model-margin", + type=float, + default=200.0, + help="Margin in Angstrom in data convention for native PHOENIX subset before RV shift and convolution", + ) + parser.add_argument("--teff-min", type=float, default=9500.0) + parser.add_argument("--teff-max", type=float, default=12500.0) + parser.add_argument("--feh-min", type=float, default=-0.5) + parser.add_argument("--feh-max", type=float, default=0.5) + parser.add_argument("--logg-min", type=float, default=3.0) + parser.add_argument("--logg-max", type=float, default=6.0) + parser.add_argument("--rv-coarse-step", type=float, default=30.0) + parser.add_argument("--rv-fine-step", type=float, default=5.0) + parser.add_argument("--rv-fine-halfspan", type=float, default=30.0) + parser.add_argument("--use-barycorr", action="store_true") + parser.add_argument("--use-telluric-mask", action="store_true") + parser.add_argument("--telluric-threshold", type=float, default=0.90) + parser.add_argument("--top-n", type=int, default=10) + parser.add_argument("--verbose", type=int, default=1) + args = parser.parse_args() + config = load_user_config() + phoenix_dir_cfg = get_config_value(config, "paths", "phoenix_dir", default=None) + + args.phoenix_dir = resolve_setting( + args.phoenix_dir, + env_var_name="SPYCTRES_PHOENIX_DIR", + config_value=phoenix_dir_cfg, + default=None, + ) + if not os.path.isfile(args.file): + parser.error("Input file not found: {0}".format(args.file)) + + if args.phoenix_dir is None: + parser.error( + "No PHOENIX directory supplied. Set --phoenix-dir, SPYCTRES_PHOENIX_DIR, " + "or [paths].phoenix_dir in ~/.config/spyctres/config.toml." + ) + + if not os.path.isdir(args.phoenix_dir): + parser.error("PHOENIX directory not found: {0}".format(args.phoenix_dir)) + + seg0 = read_spectrum(args.file, instrument="xshooter") + arm = str(seg0.meta.get("arm", "")).strip().upper() + if arm and arm != "UVB": + parser.error( + "This script expects an X-SHOOTER UVB file, but the reader reported arm={0!r}.".format(arm) + ) + seg_clip = seg0.window( + wmin=args.wmin, + wmax=args.wmax, + clip_left=args.clip_left, + clip_right=args.clip_right, + name_suffix="fitwin", + ) + + balmer_windows = [ + ("Hδ", 3980.0, 4220.0), + ("Hγ", 4220.0, 4480.0), + ("Hβ", 4700.0, 5020.0), + ] + segments = make_padded_window_segments( + seg_clip, + [(wmin, wmax) for _, wmin, wmax in balmer_windows], + pad=args.window_pad, + name_prefix="balmer", + ) + + balmer_centers_vac = { + "Hδ": 4101.74, + "Hγ": 4340.47, + "Hβ": 4861.33, + } + notebook_cont_windows = { + "Hδ": ((-80.0, -30.0), (30.0, 80.0)), + "Hγ": ((-80.0, -30.0), (30.0, 80.0)), + "Hβ": ((-120.0, -40.0), (40.0, 120.0)), + } + + for seg_i, (label, _wmin, _wmax) in zip(segments, balmer_windows): + seg_i.meta["line_label"] = label + seg_i.meta["line_center_vac"] = float(balmer_centers_vac[label]) + + seg_medium = str(seg_i.wave_medium).lower() + if seg_medium in ("air", "vacuum"): + seg_i.meta["line_center_data"] = float( + convert_wavelength_medium( + np.array([balmer_centers_vac[label]], dtype=float), + from_medium="vacuum", + to_medium=seg_medium, + )[0] + ) + else: + seg_i.meta["line_center_data"] = float(balmer_centers_vac[label]) + + seg_i.meta["cont_windows"] = notebook_cont_windows[label] + + segments = normalize_segments_sidebands( + segments, + sideband_width=args.sideband_width, + sideband_order=args.sideband_order, + ) + + seg_ref = segments[0] + exclude_mask_list = [] + + if args.use_telluric_mask: + _, telluric_mask = load_telluric_lines(args.telluric_threshold) + exclude_mask_list.append(telluric_mask) + + exclude_mask_list.append( + make_balmer_core_exclude_mask( + core_halfwidth=args.core_mask, + wave_medium=seg_ref.wave_medium, + ) + ) + + def exclude_mask(wave): + wave = np.asarray(wave, dtype=float) + m = np.zeros_like(wave, dtype=bool) + for fn in exclude_mask_list: + m |= (np.asarray(fn(wave)) > 0.5) + return m + + phoenix_lib = PhoenixLibrary(args.phoenix_dir, verbose=bool(args.verbose)) + teff_avail, feh_avail, logg_avail = phoenix_lib.available_axes() + teff_grid_req = pick_grid_range(teff_avail, args.teff_min, args.teff_max) + feh_grid_req = pick_grid_range(feh_avail, args.feh_min, args.feh_max) + logg_grid_req = pick_grid_range(logg_avail, args.logg_min, args.logg_max) + teff_grid_fit, feh_grid_fit, logg_grid_fit = phoenix_lib.complete_subgrid( + teff_grid_req, feh_grid_req, logg_grid_req + ) + + rv_bary_kms = 0.0 + if args.use_barycorr: + rv_bary_kms = float(seg_ref.meta.get("barycorr_kms") or 0.0) + + R = args.R_override if args.R_override is not None else seg_ref.meta.get("resolution_R", None) + + rv_coarse = np.arange(-300.0, 300.0 + 1e-9, args.rv_coarse_step) + template_cache = {} + rows = [] + + n_total = len(teff_grid_fit) * len(feh_grid_fit) * len(logg_grid_fit) + count = 0 + + for teff in teff_grid_fit: + for feh in feh_grid_fit: + for logg in logg_grid_fit: + count += 1 + if args.verbose: + print(f"[{count}/{n_total}] Teff={teff:.0f} [Fe/H]={feh:+.1f} logg={logg:.1f}") + + key = (float(teff), float(feh), float(logg)) + if key not in template_cache: + _wave_native, tpl_flux = phoenix_lib.load_template( + teff=float(teff), + logg=float(logg), + feh=float(feh), + wave=None, + wave_medium=None, + ) + template_cache[key] = np.asarray(tpl_flux, dtype=float) + + tpl_flux = template_cache[key] + + best = None + for rv in rv_coarse: + rec = evaluate_template_on_segments( + segments=segments, + phoenix_wave_native=phoenix_lib.phoenix_wave, + template_flux_native=tpl_flux, + rv_kms=float(rv), + rv_bary_kms=rv_bary_kms, + R=R, + exclude_mask=exclude_mask, + sideband_width=args.sideband_width, + sideband_order=args.sideband_order, + sideband_poly_order=args.sideband_poly_order, + phoenix_wave_medium=phoenix_lib.phoenix_wave_medium, + model_margin_A=args.model_margin, + return_models=False, + ) + if rec["npts"] <= 0: + continue + if (best is None) or (rec["chi2"] < best["chi2"]): + best = {"chi2": rec["chi2"], "npts": rec["npts"], "rv": float(rv)} + + if best is None: + continue + + rv0 = best["rv"] + rv_fine = np.arange( + rv0 - args.rv_fine_halfspan, + rv0 + args.rv_fine_halfspan + 1e-9, + args.rv_fine_step, + ) + for rv in rv_fine: + rec = evaluate_template_on_segments( + segments=segments, + phoenix_wave_native=phoenix_lib.phoenix_wave, + template_flux_native=tpl_flux, + rv_kms=float(rv), + rv_bary_kms=rv_bary_kms, + R=R, + exclude_mask=exclude_mask, + sideband_width=args.sideband_width, + sideband_order=args.sideband_order, + sideband_poly_order=args.sideband_poly_order, + phoenix_wave_medium=phoenix_lib.phoenix_wave_medium, + model_margin_A=args.model_margin, + return_models=False, + ) + if rec["npts"] <= 0: + continue + if rec["chi2"] < best["chi2"]: + best = {"chi2": rec["chi2"], "npts": rec["npts"], "rv": float(rv)} + + rows.append({ + "teff": float(teff), + "feh": float(feh), + "logg": float(logg), + "rv_kms": float(best["rv"]), + "chi2": float(best["chi2"]), + "npts": int(best["npts"]), + "chi2_red": np.nan if best["npts"] <= 4 else float(best["chi2"]) / (best["npts"] - 4), + }) + + if len(rows) == 0: + raise RuntimeError("No valid template evaluations were produced.") + + rows = sorted(rows, key=lambda r: r["chi2"]) + best = rows[0] + ref_point = (9800.0, -0.5, 3.0) + ref_match = None + for j, row in enumerate(rows, start=1): + if ( + abs(row["teff"] - ref_point[0]) < 1e-6 and + abs(row["feh"] - ref_point[1]) < 1e-6 and + abs(row["logg"] - ref_point[2]) < 1e-6 + ): + ref_match = (j, row) + break + + best_key = (best["teff"], best["feh"], best["logg"]) + best_tpl_flux = template_cache[best_key] + + best_eval = evaluate_template_on_segments( + segments=segments, + phoenix_wave_native=phoenix_lib.phoenix_wave, + template_flux_native=best_tpl_flux, + rv_kms=best["rv_kms"], + rv_bary_kms=rv_bary_kms, + R=R, + exclude_mask=exclude_mask, + sideband_width=args.sideband_width, + sideband_order=args.sideband_order, + sideband_poly_order=args.sideband_poly_order, + phoenix_wave_medium=phoenix_lib.phoenix_wave_medium, + model_margin_A=args.model_margin, + return_models=True, + ) + + print("File:", args.file) + print("Object:", seg_ref.meta.get("object")) + print("Arm:", seg_ref.meta.get("arm")) + print("R used:", R) + print("Barycorr used [km/s]:", rv_bary_kms) + print("Best discrete-template fit:") + print(" Teff =", best["teff"]) + print(" [Fe/H] =", best["feh"]) + print(" logg =", best["logg"]) + print(" RV =", best["rv_kms"]) + print(" chi2 =", best["chi2"]) + print(" npts =", best["npts"]) + print(" chi2_red approx =", best["chi2_red"]) + print("Top results:") + for row in rows[:args.top_n]: + print( + " Teff={0:.0f} [Fe/H]={1:+.1f} logg={2:.1f} RV={3:+.1f} chi2={4:.1f} chi2_red~{5:.3f}".format( + row["teff"], row["feh"], row["logg"], row["rv_kms"], row["chi2"], row["chi2_red"] + ) + ) + if ref_match is not None: + j, row = ref_match + print("Notebook-like reference point:") + print( + " rank={0} Teff={1:.0f} [Fe/H]={2:+.1f} logg={3:.1f} RV={4:+.1f} chi2={5:.1f} delta_chi2={6:.1f}".format( + j, + row["teff"], + row["feh"], + row["logg"], + row["rv_kms"], + row["chi2"], + row["chi2"] - best["chi2"], + ) + ) + print("Per-window chi2_red:") + for k, v in best_eval["per_window"].items(): + print( + " {0}: chi2_red={1} N={2} range=({3:.1f},{4:.1f})".format( + k, v["chi2_red"], v["n"], v["wave_min"], v["wave_max"] + ) + ) + + title = ( + f"{os.path.basename(args.file)} discrete PHOENIX scan " + f"Teff={best['teff']:.0f} [Fe/H]={best['feh']:.2f} " + f"logg={best['logg']:.2f} RV={best['rv_kms']:.1f} " + f"chi2_red~={best['chi2_red']:.2f}" + ) + + fig, axes = plt.subplots(2, 2, figsize=(12, 8)) + axes = axes.ravel() + + for ax, (label, _, _), seg_i, model_i, excl_i in zip( + axes, balmer_windows, segments, best_eval["model_corr_list"], best_eval["excluded_masks"] + ): + ax.plot(seg_i.wave, seg_i.flux, lw=1.0, label="Data") + ax.plot(seg_i.wave, model_i, lw=1.0, label="Model") + + if np.any(excl_i): + y0, y1 = ax.get_ylim() + ax.fill_between(seg_i.wave, y0, y1, where=excl_i, alpha=0.15) + ax.set_ylim(y0, y1) + + ax.set_title(label) + ax.set_xlabel("Wavelength (Å)") + ax.set_ylabel("Flux") + ax.legend(loc="best") + + if len(axes) > len(segments): + for ax in axes[len(segments):]: + ax.axis("off") + + fig.suptitle(title) + fig.tight_layout() + plt.show() + + +if __name__ == "__main__": + main() diff --git a/setup.py b/setup.py index 7da7323..c516a0c 100755 --- a/setup.py +++ b/setup.py @@ -11,14 +11,15 @@ url="https://github.com/ebachelet/Spyctres", download_url = '', install_requires=['scipy','numpy','matplotlib','astropy','speclite','pysynphot'], - python_requires='>=3.6,<4', + python_requires='>=3.12,<4', test_suite="nose.collector", classifiers=[ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Topic :: Software Development :: Build Tools', 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)', - 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.12', ], zip_safe=False, packages=find_packages('.'),