📘 Getting StartedÂļ

Computing featuresÂļ

To compute features on your data, a FeatureCalculator object must first be instantiated using:

from pyhctsa.calculator import FeatureCalculator
calc = FeatureCalculator()

By default, the FeatureCalculator will initialize the full feature set. If you would like to specify a custom feature set, you can pass the corresponding configuration .YAML file as an argument to the FeatureCalculator:

custom_calc = FeatureCalculator(config_path="subset.yaml")

The number of master operations (callable functions) specified by the .yaml will be displayed for verification e.g., Loaded 700 master operations. Once a FeatureCalculator has been initialized, you can call the extract method to compute time series features on either a single time-series instance or a list of multiple instances:

from pyhctsa.utils import get_dataset

e1000 = get_dataset()
data = e1000[0] # your data as a list, array, or pandas series
res = calc.extract(data)

Note that each time-series instances does not have to be the same length to compute a vector of features. The results of the extraction will be returned in a pandas dataframe of shape N×F, where N is the number of time-series instances and F is the number of time-series features.

🤖 Calling Individual OperationsÂļ

If you would like to run individual operations on your data, you can access the corresponding functions from their respective modules directly. For example, to compute the raw_hrv_meas features on your data, the raw_hrv_meas master operation can be accessed from the medical module:

from pyhctsa.operations.medical import raw_hrv_meas
data = ... # your ArrayLike data
res = raw_hrv_meas(data) # result as either a dictionary or scalar value

Note that individual operations can only be called directly on individual time-series instances.

🏗ī¸ Parallel ComputingÂļ

Time-series feature extraction is computationally intensive. To speed up processing, pyhctsa allows you to distribute the workload across multiple CPU cores on your local machine using the LocalDistributor:

from pyhctsa.distributed import LocalDistributor
from pyhctsa.calculator import FeatureCalculator

# initialize the calculator
calc = FeatureCalculator()

# create a LocalDistributor and specify the number of workers
# it is generally recommended to set n_workers to the number of physical CPU cores
dist = LocalDistributor(n_workers=4)

# pass the distributor to the .extract() method
res = calc.extract(data, distributor=dist)

ℹī¸ Note for Windows UsersÂļ

Some features require Java (JDK) to be installed. If you encounter a JVM not found error:
  1. Ensure Java Development Kit (JDK) is installed on your system

    • Download from Oracle or use OpenJDK (Minimum version required: JDK 11)

  2. Before importing pyhctsa, set the JAVA_HOME environment variable using the location of the JDK installation on your system:

import os
os.environ['JAVA_HOME'] = "C:\Program Files\Java\jdk-11" # replace with relevant path
from pyhctsa.calculator import FeatureCalculator
# rest of your code...