rfwtools.extractor.tsf

This module provides tsfresh-based time series statistical feature extraction tools.

Typically, these will be used by DataSet.produce_feature_set(). However there is no reason why these can’t be run externally.

Basic Usage Example:

from rfwtools.data_set import DataSet
from rfwtools.extractor.tsf import tsfresh_extractor

# Setup a DataSet object and get some example data to work with
ds = DataSet()
ds.load_example_set_csv("my_example_set.csv")

# Get a single example to work on
ex = ds.example_set.loc[0, 'example']

# Run on one example with defaults
tsfresh_extractor(ex)
# Run on one example with only 2 signals being processed
tsfresh_extractor(ex, signals=['1_GMES', '1_PMES'])
# Run on one example, but only include values before the fault on set.
tsfresh_extractor(ex, query="Time < 0")

# Run this on every example in the example set and produce a corresponding feature set for pre-fault signal data.
ds.produce_feature_set(tsfresh_extractor, query="Time < 0")

Functions

tsfresh_extractor(example[, signals, query, ...])	Use tsfresh to extract features specified.
tsfresh_extractor_faulted_cavity(example[, ...])	Use tsfresh to extract features for only the cavity that faulted.