Linear trend features¶

`light_curve.LinearFit` ¶

Bases: _FeatureEvaluator

Slope, its error and reduced \(\chi^2\) of the light curve in the linear fit

Least squares fit of the linear stochastic model with Gaussian noise described by observation errors \(\{\delta_i\}\):

\[ m_i = c + \mathrm{slope} t_i + \delta_i \varepsilon_i \]

where \(c\) is a constant, \(\{\varepsilon_i\}\) are standard distributed random variables.

Feature values are \(\mathrm{slope}\), \(\sigma_\mathrm{slope}\) and \(\frac{\sum{((m_i - c - \mathrm{slope} t_i) / \delta_i)^2}}{N - 2}\).

Depends on: time, magnitude, magnitude error
Minimum number of observations: 3
Number of features: 3

Parameters:

Name	Type	Description	Default
`transform`	`str or bool or None`	Transformer to apply to the feature values. If str, must be one of: `'default'` - use default transformer for the feature, same as giving True. The default for this feature is `'identity'` `'arcsinh'` - Hyperbolic arcsine feature transformer `'clipped_lg'` - Decimal logarithm of a value clipped to a minimum value `'identity'` - Identity feature transformer `'lg'` - Decimal logarithm feature transformer `'ln1p'` - ln(1+x) feature transformer `'sqrt'` - Square root feature transformer If bool, True uses the default transformer, False disables it. If None, no transformation is applied (default)	`None`
`bands`	`list of str or None`	Passband names for multiband mode. If provided, the feature is evaluated independently per passband and the outputs are concatenated in passband order. If None (default), single-band mode is used.	required

Attributes:

Name	Type	Description
`names`	`list of str`	Feature names
`descriptions`	`list of str`	Feature descriptions
`bands`	`numpy.ndarray of str or None`	Passband names for multiband mode, or None for single-band mode

Methods:

Name	Description
`__call__`	Extract features and return them as a numpy array
`many`	Extract features from multiple light curves in parallel

`light_curve.LinearTrend` ¶

Bases: _FeatureEvaluator

The slope, its error and noise level of the light curve in the linear fit

Least squares fit of the linear stochastic model with constant Gaussian noise \(\Sigma\) assuming observation errors to be zero:

\[ m_i = c + \mathrm{slope} t_i + \Sigma \varepsilon_i, \]

where \(c\) is a constant, \(\{\varepsilon_i\}\) are standard distributed random variables. \(\mathrm{slope}\), \(\sigma_\mathrm{slope}\) and \(\Sigma\) are returned.

Depends on: time, magnitude
Minimum number of observations: 3
Number of features: 3