Output Files

pykurucz produces three primary output formats: .spec (the synthetic spectrum), .atm (the model atmosphere), and .npz (the preprocessed atmosphere cache). This page explains the contents of each and how to load them in Python.

.spec — Synthetic Spectrum

The .spec file is the final scientific product: a whitespace-delimited text file with three columns.

Column	Name	Units	Description
1	Wavelength	nm (vacuum)	Wavelength of each sample point
2	\(F_\lambda\)	erg cm⁻² s⁻¹ nm⁻¹	Total emergent flux (line + continuum)
3	\(F_{\rm cont}\)	erg cm⁻² s⁻¹ nm⁻¹	Continuum-only flux

The normalized spectrum is \(F_\lambda / F_{\rm cont}\). Wavelengths are uniformly spaced in log-space according to the requested resolving power.

Reading a .spec file

import numpy as np

wl, flux, cont = np.loadtxt("results/spec/t05770g4.44_mh+0.00_am+0.00_500_510.spec", unpack=True)

# Normalized spectrum
norm = flux / cont

Plotting

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 4))
plt.plot(wl, norm, lw=0.5)
plt.xlabel("Wavelength (nm)")
plt.ylabel(r"$F_\lambda / F_{\rm cont}$")
plt.ylim(0, 1.1)
plt.tight_layout()
plt.savefig("spectrum.png", dpi=200)

.atm — Model Atmosphere

The .atm file is a Kurucz-format ASCII file that completely describes the model atmosphere. It contains:

• Header cards: TEFF, GRAVITY, TITLE, OPACITY IFOP ..., CONVECTION ...
• Abundance cards: ABUNDANCE CHANGE and ABUNDANCE TABLE for elements 1–99
• DECK6 block: 80 layers of atmospheric structure

DECK6 format

The READ DECK6 block has 80 rows (layers) and 9 columns:

Column	Symbol	Units	Description
1	RHOX	g cm⁻²	Mass column density
2	T	K	Temperature
3	P	dyn cm⁻²	Gas pressure
4	XNE	cm⁻³	Electron number density
5	ABROSS	cm² g⁻¹	Rosseland mean opacity
6	ACCRAD	cm s⁻²	Radiative acceleration
7	VTURB	cm s⁻¹	Microturbulent velocity
8	FLXCNV	—	Convective flux ratio (often 0)
9	VCONV	cm s⁻¹	Convective velocity (often 0)

Reading an .atm file

from atlas_py.io.atmosphere import load_atm

atm = load_atm("results/atm/t05770g4.44_mh+0.00_am+0.00.atm")

print(atm.temperature)      # shape (80,)
print(atm.gas_pressure)     # shape (80,)
print(atm.electron_density) # shape (80,)
print(atm.rhox)             # shape (80,)

Two atmosphere files in Stellar Parameters

Stellar Parameters writes both <stem>_warmstart.atm (the raw emulator prediction) and <stem>.atm (the atlas_py iterated atmosphere). For science, always use the iterated atmosphere.

.npz — Preprocessed Atmosphere Cache

The .npz file is a NumPy archive produced by convert_atm_to_npz.py. It stores precomputed quantities that accelerate synthesis:

• depth — Rosseland optical depth grid
• temperature — layer temperatures (K)
• gas_pressure — gas pressures (dyn cm⁻²)
• electron_density — electron densities (cm⁻³)
• mass_density — mass densities (g cm⁻³)
• turbulent_velocity — microturbulent velocity (km/s)
• population_per_ion — Saha–Boltzmann populations for all ions
• doppler_per_ion — Doppler widths per ion
• xnfph — hydrogen ground-state populations
• xnf_he1 / xnf_he2 — helium populations
• xnfpc, xnfpmg, xnfpal, xnfpsi, xnfpfe — metal ground-state populations
• xabund — abundance mass fractions
• cont_abs_coeff / cont_scat_coeff — continuum opacity interpolation coefficients
• meta_* — metadata (Teff, logg, title, abundances, version stamp)

Loading a .npz file

import numpy as np

data = np.load("results/npz/t05770g4.44_mh+0.00_am+0.00.npz")
print(data.files)          # list of arrays
print(data["temperature"]) # shape (80,)

Cache invalidation

The .npz file is tied to a specific .atm file by content, not just by filename. If you edit the .atm abundances or temperature structure, you must regenerate the .npz. synthe_py checks a version stamp inside the .npz and will auto-refresh stale caches if the converter script is available.

Log Files

Both atlas_py and synthe_py write detailed logs:

• logs/<stem>_atlas.log — iteration timings, convergence diagnostics, opacity summaries
• logs/<stem>_synthe_<wl0>_<wl1>.log — line counts, wavelength progress, worker timings

These are invaluable for debugging convergence issues or understanding runtime bottlenecks.

Summary Table

Extension	Producer	Consumer	Purpose
.spec	synthe_py.cli	User / analysis scripts	Final synthetic spectrum
.atm	atlas_py.cli / emulator	convert_atm_to_npz.py	Model atmosphere stratification
.npz	convert_atm_to_npz.py	synthe_py.cli	Precomputed populations and opacities

Next Steps

• Explore the CLI Reference to control output paths and filenames.
• Learn about the Emulator to understand the warm-start atmosphere.
• Dive into Architecture for the full data-flow diagram.