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name: modeling-stats-timeseries description: "Use Astropy modeling, fitting, robust statistics, histograms, time series, periodograms, and uncertainty helpers for astronomy analysis." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

Modeling, Stats, and Time Series Router

Use this sub-skill when a task centers on Astropy models, fitters, robust statistics, periodograms, or time-series containers.

Load This When

• The task mentions astropy.modeling, predefined models, Gaussian1D, polynomials, compound models, parameter constraints, bounds, tied parameters, or fitters.
• The task needs sigma_clip, sigma_clipped_stats, mad_std, biweight statistics, histograms, Bayesian blocks, or circular statistics.
• The task uses TimeSeries, BinnedTimeSeries, LombScargle, BoxLeastSquares, periodograms, false-alarm probabilities, or time-binned light curves.
• The task needs uncertainty distributions from astropy.uncertainty.

Route Away When

• Creating times or coordinates is the main issue; use ../time-coordinates/SKILL.md.
• Units and equivalencies are the main obstacle; use ../units-constants/SKILL.md.
• Plotting fit results or images is central; use ../visualization-convolution/SKILL.md.
• Table file formats dominate; use ../tables-io/SKILL.md.

First Actions

1. Identify input arrays/tables, units, masks, uncertainties, and time scale.
2. For fitting, choose a model class and fitter compatible with constraints and linear/nonlinear behavior.
3. Initialize parameters near plausible values; set bounds/fixed/tied flags before fitting.
4. For outliers, use sigma clipping or robust stats before or during fitting.
5. For periodograms, make time and frequency units explicit and choose the normalization/statistical interpretation.
6. Validate with residuals, parameter units, output shapes, finite values, and a small synthetic check.

References

• references/api-reference.md lists model, fitter, stats, and periodogram APIs.
• references/workflows.md gives fitting, robust statistics, histogram, time-series, and periodogram recipes.
• references/troubleshooting.md covers bad initial guesses, unit mismatches, optional SciPy behavior, periodogram pitfalls, and masked/NaN data.

Safety and Validation

• Do not treat a fitted parameter as valid without checking residuals and parameter units.
• Do not silently drop masks/NaNs; choose clipping or filtering explicitly.
• Use bounded/synthetic data for smoke tests; avoid long searches or benchmark grids unless requested.
• For time series, preserve time scale and units through period/frequency calculations.

Native-Backed Validation Ideas

• Fit Linear1D or Gaussian1D to small synthetic data and assert parameters.
• Apply sigma_clip to [1, 1, 100] and assert the outlier is masked.
• Run a tiny LombScargle power calculation and assert finite output.