API Reference¶

TessQuickLook¶

The main class that runs the full pipeline.

from quicklook import TessQuickLook

Constructor¶

TessQuickLook(
    target_name: str,
    sector: int = -1,
    pipeline: str = "SPOC",
    flux_type: str = "pdcsap",
    exptime: float = None,
    pg_method: str = "gls",
    flatten_method: str = "biweight",
    gp_kernel: str = "periodic_auto",
    gp_kernel_size: float = 5,
    window_length: float = None,
    edge_cutoff: float = 0.1,
    sigma_clip_raw: tuple = None,
    sigma_clip_flat: tuple = None,
    custom_ephem: list = None,
    mask_ephem: bool = False,
    Porb_limits: tuple = None,
    archival_survey: str = "dss1",
    show_plot: bool = True,
    verbose: bool = True,
    savefig: bool = False,
    savetls: bool = False,
    overwrite: bool = False,
    suffix: str = None,
    quality_bitmask: str = "default",
    outdir: str = ".",
)

Parameters:

Parameter	Type	Default	Description
`target_name`	str	required	Target name (TOI, TIC, or common name)
`sector`	int	`-1`	TESS sector (-1 = latest available)
`pipeline`	str	`"SPOC"`	Light curve pipeline
`flux_type`	str	`"pdcsap"`	Flux column: `"pdcsap"` or `"sap"`
`exptime`	float	`None`	Exposure time in seconds (auto-detected if None)
`pg_method`	str	`"gls"`	Periodogram method: `"gls"`, `"ls"`, `"bls"`
`flatten_method`	str	`"biweight"`	Detrending method (any wotan method)
`gp_kernel`	str	`"periodic_auto"`	GP kernel (if flatten_method is `"gp"`)
`gp_kernel_size`	float	`5`	GP kernel size
`window_length`	float	`None`	Detrending window in days (auto if None)
`edge_cutoff`	float	`0.1`	Days to cut from orbit edges
`sigma_clip_raw`	tuple	`None`	`(lo, hi)` sigma clip on raw light curve
`sigma_clip_flat`	tuple	`None`	`(lo, hi)` sigma clip on flattened light curve
`custom_ephem`	list	`None`	Custom ephemeris: `[Tc, Tc_err, P, P_err, dur, dur_err]`
`mask_ephem`	bool	`False`	Mask known transits before detrending
`Porb_limits`	tuple	`None`	`(min, max)` TLS period search range in days
`archival_survey`	str	`"dss1"`	Sky survey for image overlay
`show_plot`	bool	`True`	Display the figure interactively
`verbose`	bool	`True`	Print progress messages
`savefig`	bool	`False`	Save figure to disk
`savetls`	bool	`False`	Save TLS results to HDF5
`overwrite`	bool	`False`	Overwrite existing output files
`suffix`	str	`None`	Suffix for output filenames
`quality_bitmask`	str	`"default"`	TESS quality bitmask
`outdir`	str	`"."`	Output directory

Key attributes¶

After instantiation, these attributes are available:

Attribute	Type	Description
`raw_lc`	`lightkurve.LightCurve`	Raw light curve
`flat_lc`	`lightkurve.LightCurve`	Flattened light curve
`trend_lc`	`lightkurve.LightCurve`	Trend component
`tls_results`	`transitleastsquares.results`	TLS search results
`Prot_ls`	float	Stellar rotation period from GLS (days)
`tpf`	`lightkurve.TargetPixelFile`	Target pixel file
`sector`	int	TESS sector number
`pipeline`	str	Pipeline name (lowercase)
`cadence`	str	`"short"` or `"long"`
`exptime`	float	Exposure time in seconds
`gaiaid`	int	Gaia DR2/DR3 source ID
`tic`	int	TIC ID

Methods¶

`plot_tql()`¶

Generate the 9-panel diagnostic figure.

fig = ql.plot_tql()

Returns a matplotlib.figure.Figure. If savefig=True, the figure is also saved to disk.

Exceptions¶

from quicklook.exceptions import QuickLookError, NoDataError, InvalidInputError, PipelineError

Exception	Raised when
`QuickLookError`	Base exception for all pipeline errors
`NoDataError`	No light curves, TPFs, or sectors found for the target
`InvalidInputError`	Invalid user input (bad ephemeris, unsupported parameters)
`PipelineError`	Unrecoverable processing error during the pipeline

Utility functions¶

from quicklook.utils import get_exofop_json, get_available_sectors

`get_exofop_json(target_name)`¶

Query ExoFOP-TESS and return the full JSON response as a dict.

data = get_exofop_json("TOI-1234")

`get_available_sectors(target_name, pipeline="SPOC")`¶

Return a list of TESS sectors with available light curves for the target.

sectors = get_available_sectors("TOI-1234", pipeline="SPOC")
# [27, 56]

GLS periodogram¶

from quicklook import Gls

The Generalized Lomb-Scargle periodogram implementation. Used internally by TessQuickLook but also available for standalone use.

gls = Gls(data, Pbeg=0.5, Pend=15.0, verbose=False)
print(f"Best period: {gls.best['P']:.3f} days")
print(f"FAP: {gls.FAP():.2e}")