1. The Physio data object

The primary funtionality of peakdet relies on its operations being performed on physiological data loaded into a peakdet.Physio object. So, before we get into using peakdet, its best to understand a little bit about this helper class!

All you need to create a Physio object is a data array:

>>> import numpy as np
>>> from peakdet import Physio
>>> data = np.random.rand(5000)
>>> phys = Physio(data)
>>> print(phys)
Physio(size=5000, fs=nan)

However, it is strongly recommended that you provide the sampling rate of the data as well. Many functions in peakdet require this information:

>>> sampling_rate = 100.0
>>> phys = Physio(data, fs=sampling_rate)
>>> print(phys)
Physio(size=5000, fs=100.0)

A Physio object is designed to be lightweight, and mostly exists to store raw physiological data and its corresponding sampling rate in the same place. In most instances it can be treated like a one-dimensional numpy.ndarray; the underlying data can be accessed via slicing and the object can be passed directly to most numpy functions:

>>> phys[:5]
array([0.19151945, 0.62210877, 0.43772774, 0.78535858, 0.77997581])
>>> np.mean(phys)
0.4991523987519155

Beyond being a simple container, however, Physio objects have a few attributes that are of interest when working with real physiological data. Importantly, they have a history that records all operations performed on the data:

>>> from peakdet import operations
>>> phys = operations.filter_physio(phys, cutoffs=0.1, method='lowpass')
>>> phys.history
[('filter_physio', {'cutoffs': 0.1, 'method': 'lowpass'})]

Moreover, if you perform peak finding on a Physio object it will store the indices of the detected peaks and troughs alongside the object:

>>> phys = operations.peakfind_physio(phys)
>>> phys.peaks
array([ 477, 2120, 3253])
>>> phys.troughs
array([1413, 2611])

Next, we’ll move on to how you can load your data into a Physio object in a more reproducible manner. Feel free to refer to the API for more information.