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ASCII table .csv

A text file: a few header lines that start with #, then four columns separated by commas, one row per wavelength (241 rows). All values are in SI units.

Column	Quantity	Unit
1	Wavelength	m
2	Scattering cross section	m²
3	Absorption cross section	m²
4	Extinction cross section	m²

Mind the order: scattering comes before absorption, and extinction is last. Extinction = scattering + absorption.

# Condensate: Forsterite
# Mode: compact
# Number of spheres: 16
# Target Equivalent Radius: 1.0um, Monomer Sphere Radius: 0.40 um
# Calculation: nptm-code np_cluster
# Data format: Wavelength [m], Scattering Cross Sec[m2], Absorption Cross Sec[m2], Extinction Cross Sec [m2]

1.0000000E-06,1.2387046E-11,1.4463832E-14,1.2401509E-11
1.1000000E-06,1.3601055E-11,1.1769628E-14,1.3612825E-11

The header says which species and shape it is, how many monomers the grain has (1 for a single sphere), the equivalent radius, the radius of one monomer and the code used. In Python: numpy.loadtxt(file, delimiter=",") gives an array of 241 rows × 4 columns (the # lines are skipped).

JSON .json

The same curve, ready for plotting, in other units: wavelength in µm and cross sections in cm² (1 m² = 10⁴ cm²).

{"label": "Mg2SiO4 compact aggregate r=1 um (16 monomers)",
 "lambda_um": [1.0, 1.1, ...],   // 241 values, µm
 "ext": [...],  "abs": [...],  "sca": [...]   // 241 values each, cm²
}

Structure of the grain .geo.json

Only for aggregates: where the monomers are, so that you can draw the grain.

{"monomer_um": 0.4,          // radius of one monomer, µm
 "radius_um": 1.0,           // equivalent radius of the whole grain, µm
 "positions_um": [[x, y, z], ...]   // one centre per monomer, µm
}

Binary file .npz

The whole database in one file, for Python: curves as NumPy arrays grouped by species and shape, in µm and m². Its layout and the functions to read it are described inside csdb.py.