asteroid_spinprops 1.3.0

Collection of tools used for fitting sHG1G2 and SOCCA photometric models to sparse asteroid photometry

asteroid-spinprops

Overview

asteroid-spinprops is a Python package providing tools to fit SHG1G2 and SOCCA photometric models to sparse asteroid photometry.
It supports multiband modeling, residual analysis and shape, period and pole orientation estimation for small solar system objects.

---

Installation

Install the package via pip:

pip install asteroid_spinprops

Input column requirements and preprocessing

asteroid_spinprops expects photometric measurements to follow the Fink alert schema.
If your DataFrame uses different column names, they must be renamed to the standard format before analysis.

The package maps common input columns to Fink-style fields:

Expected name	Description
cjd	Observation time (JD)
cmagpsf	PSF magnitude
csigmapsf	Magnitude uncertainty
cfid	Filter identifier
ra	Right ascension (deg)
dec	Declination (deg)
Phase	Solar phase angle (deg)

Additional columns created during preprocessing

The preprocessing step also adds the following fields:

• cmred — Reduced magnitude

  Computed from the heliocentric and observer-centric distances:

$$ \mathrm{cmred} = \mathrm{cmagpsf} - 5\log_{10}!\left(\frac{r,\Delta}{\mathrm{AU}^2}\right) $$
where Obj_Sun_LTC_km = (r) and Range_LTC_km = (\Delta).

• jd_ltc — Light-time–corrected Julian Date

  First converts MJD → JD (+ 2400000.5), then applies the correction

  $$ JD_\mathrm{ltc} = JD - \frac{\Delta}{c}, $$

  using the one-way light-travel time in days.

pdf.rename(
    columns={
        "Your_JD_column": "cjd",
        "Your_magnitudes_column": "cmagpsf",
        "Your_phase_angle_column": "Phase",
        "Your_RA_column": "ra",
        "Your_Dec_column": "dec",
        "Your_magnitude_uncertainty_column": "csigmapsf",
        "Your_filter_column": "cfid",
    },
    inplace=True,
)

# Add missing columns
pdf["cmred"] = pdf["cmagpsf"] - 5 * np.log10(
    pdf["Observer_SSO_distance_column"] * pdf["Sun_SSO_distance_column"] / (au**2)
)

# LT correction
pdf["cjd"] = pdf["cjd"] + 2400000.5  # MJD to JD
pdf["jd_ltc"] = pdf["cjd"] - pdf["Observer_SSO_distance_column"] / c_kmday  # light time correction

Required inputs

Your input DataFrame must therefore include:

• time of observation (JD)
• PSF magnitude and uncertainty
• filter ID
• RA, Dec (Degrees)
• phase angle
• heliocentric distance (AU)
• observer-centric distance (AU)

The preprocessing step renames these fields to the Fink schema, computes reduced magnitudes, and applies the light-time correction to the observation timestamps.

Quick Start

import numpy as np
import pandas as pd
from asteroid_spinprops.ssolib import dataprep, periodest, modelfit

# Suppose `pdf` is your initial asteroid DataFrame 
# Ensure all columns are converted to the required single row format.
pdf_s = pd.DataFrame({col: [np.array(pdf[col])] for col in pdf.columns})

# Convert filter IDs to numeric
unique_vals, inv = np.unique(pdf_s["cfid"].values[0], return_inverse=True)
numeric_filter = inv + 1
pdf_s["cfid"].values[0] = numeric_filter

# --- Data cleaning and filtering ---
clean_data, errorbar_rejects = dataprep.errorbar_filtering(data=pdf_s, mlimit=0.7928) # mag limit from the LCDB
clean_data, projection_rejects = dataprep.projection_filtering(data=clean_data)
clean_data, iterative_rejects = dataprep.iterative_filtering(data=clean_data)

# --- Fit SOCCA ---
SOCCA_params = modelfit.get_fit_params(
        data=clean_data,
        flavor="SOCCA",
        shg1g2_constrained=True,
        pole_blind=False,
        period_blind=True,
        period_in=None,
        period_quality_flag=True
    )


## --- Or step-by-step --- ##
# --- Fit SHG1G2 model ---
shg1g2_params = modelfit.get_fit_params(
    data=clean_data,
    flavor="SHG1G2",
)

# Compute residuals for period analysis
residuals_dataframe = modelfit.make_residuals_df(
    clean_data, model_parameters=shg1g2_params
)

# --- Estimate rotation period ---
p_in, k_val, p_rms, signal_peak, window_peak = periodest.get_multiband_period_estimate(
    residuals_dataframe,
    k_free=True,
)

# Assess period robustness via bootstrap resampling
_, Nbs = periodest.perform_residual_resampling(
    resid_df=residuals_dataframe,
    p_min=0.03,
    p_max=2,
    k=int(k_val)
)

# --- Fit SOCCA model ---
SOCCA_params = modelfit.get_fit_params(
    data=clean_data,
    flavor="SSHG1G2",
    shg1g2_constrained=True,
    period_blind=False,
    pole_blind=False,
    period_in=p_in,
    period_quality_flag=False
)

Models

Photometric models from Carry et al.(2024) {2024A&A...687A..38C} and https://github.com/astrolabsoftware

Project status

Under development