Python API

This is the class and function reference of pycatch22. Please refer to the user guide for further details as the class and function specifications may not be sufficient to give full context.

catch22_all

Extract the catch22 feature set from an input time series.

Syntax

pycatch22.catch22_all(data, catch24=False, short_names=False)

Parameters

Parameter	Type	Description
data	array-like	Input time-series data.
catch24	bool	If True, include the two catch24 features (mean and standard deviation) in the output. Default is False.
short_names	bool	If True, also include the short names of the features in the output. Default is False.

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Return Value

The function returns a dictionary with the following keys:

Key	Type	Description
'names'	list	List of feature names.
'values'	list	List of corresponding feature values.
'short_names'	list	List of short feature names (if short_names=True).

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Example Usage

import numpy as np
import pycatch22 as catch22

data = np.random.rand(100)
features = catch22_all(data)
print(features['names'])
# Output: ['DN_HistogramMode_5', 'DN_HistogramMode_10', 'CO_f1ecac', ..., 'SP_Summaries_welch_rect_centroid', 'FC_LocalSimple_mean3_stderr']
print(features['values'])
# Output: [0.23, 0.18, 1.52, ..., 0.04, 0.67]

features_with_catch24 = catch22_all(data, catch24=True)
print(features_with_catch24['names'][-2:])
# Output: ['DN_Mean', 'DN_Spread_Std']

features_with_short_names = catch22_all(data, short_names=True)
print(features_with_short_names['short_names'])
# Output: ['mode_5', 'mode_10', 'acf_timescale', ..., 'low_freq_power', 'forecast_error']

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Individual Feature Methods

The catch22 module provides direct access to the individual feature extraction methods implemented in C. These methods can be called directly using pycatch22.{name}, where {name} is the name of the feature method (as given by the long name in the table of features).

Syntax

pycatch22.DN_Mean(data)

Parameters

Parameter	Type	Description
data	(list or tuple)	Input time-series data.

Return Values

Value	Type	Description
feature	(Int or Float)	Individual feature value. Either integer or float depending on the feature method.

Example Usage

import numpy as np
from pycatch22 import DN_Mean

data = list(np.random.randn(100))
individual_feature = DN_Mean(data)
print(individual_feature)
# Output: -0.11125506527054776

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