Skip to content

Detection-based features

These features require a non-detection column passed as sigma (set to 0 for non-detections).

light_curve.FluxNNotDetBeforeFd

Bases: light_curve.light_curve_py.features._base.BaseSingleBandFeature

Number of non-detections before the first detection for measurements of the flux.

Feature use a user-defined signal to noise ratio to define non-detections and count their number before the first detection. strictly_fainter flag allows counting non-detections with a strictly smaller upper limit than the first detection flux (there is no such feature in the original article).

  • Depends on: flux
  • Minimum number of observations: 2
  • Number of features: 1

Attributes:

Name Type Description
signal_to_noise float

Signal to noise ratio.
strictly_fainter bool

Flag to determine if to find non-detections with strictly smaller upper limit than the first detection flux.
P. Sánchez-Sáez et al 2021, [DOI 10.3847/1538-3881/abd5c1](https://doi.org/10.3847/1538-3881/abd5c1)

signal_to_noise = 5.0 class-attribute

Convert a string or number to a floating-point number, if possible.

strictly_fainter = False class-attribute

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

light_curve.MagnitudeNNotDetBeforeFd

Bases: light_curve.light_curve_py.features._base.BaseSingleBandFeature

Number of non detections before the first detection for measurements of the magnitude.

Feature use a user-defined value to mark non-detections: measurements with sigma equal to this value considered as non detections. strictly_fainter flag allows counting non-detections with a strictly larger upper limit than the first detection magnitude (there is no such feature in the original article).

  • Depends on: magnitude
  • Minimum number of observations: 2
  • Number of features: 1

Attributes:

Name Type Description
sigma_non_detection float

Sigma value to mark the non detections values, may not be NaN.
strictly_fainter bool

Flag to determine if to find non-detections with strictly larger upper limit than the first detection magnitude.
P. Sánchez-Sáez et al 2021, [DOI 10.3847/1538-3881/abd5c1](https://doi.org/10.3847/1538-3881/abd5c1)

sigma_non_detection = inf class-attribute

Convert a string or number to a floating-point number, if possible.

strictly_fainter = False class-attribute

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.