Time-sampling features

light_curve.Duration

Bases: _FeatureEvaluator

Time-series duration

\[ t_{N-1} - t_0. \]

Note: cadence-dependent feature.

• Depends on: time
• Minimum number of observations: 1
• Number of features: 1

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.MaximumTimeInterval

Bases: _FeatureEvaluator

Maximum time interval between consequent observations

\[ \max{(t_{i+1} - t_i)} \]

Note: highly cadence-dependent feature.

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

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.MinimumTimeInterval

Bases: _FeatureEvaluator

Minimum time interval between consequent observations

\[ \min{(t_{i+1} - t_i)} \]

Note: highly cadence-dependent feature.

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

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.ObservationCount

Bases: _FeatureEvaluator

Number of observations

\[ N \]

Note: cadence-dependent feature.

• Depends on: nothing
• Minimum number of observations: 0
• Number of features: 1

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.TimeMean

Bases: _FeatureEvaluator

Mean time

\[ \langle t \rangle \equiv \frac1{N} \sum_i {t_i}. \]

Note: highly cadence-dependent feature.

• Depends on: time
• Minimum number of observations: 1
• Number of features: 1

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.TimeStandardDeviation

Bases: _FeatureEvaluator

Standard deviation of time moments

\[ \sigma_t \equiv \frac{\sum_i {(t_i - \langle t \rangle)^2}}{N - 1}. \]

Note: highly cadence-dependent feature.

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

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