# Getting started

physioKIT is a Python library for processing sampled physiological signals. It can generate synthetic examples, clean recordings, find events such as ECG R-peaks, and calculate derived measures. You can follow the first example without a dataset or sensor. When you have your own recording, use its actual sampling rate with each processing function.

## Choose a first step

- [Install and run a first analysis](https://ambiqai.github.io/physiokit/tutorial/quickstart/): Install with uv or pip, generate a synthetic ECG, find R-peaks, and calculate HRV measures.
- [Use your own data](https://ambiqai.github.io/physiokit/tutorial/your-data/): Load one ECG channel, check its shape and sampling rate, then locate peaks in your recording.

## How the workflow fits together

1. **Provide samples.** Start with a generated signal or load an array from your own recording. Record the sampling rate in hertz (samples per second).
2. **Prepare the signal.** Choose the cleaning routine for the signal type and pass the sampling rate used to collect the data.
3. **Find events and calculate measures.** For ECG, R-peaks are returned as sample positions. Their spacing becomes RR intervals; the sampling rate converts those intervals to time-based measures.
4. **Inspect the result.** Compare intermediate signals and detected events before using a derived value. The [signal guides](https://ambiqai.github.io/physiokit/reference/) pair code examples with saved plots, while the [API Reference](https://ambiqai.github.io/physiokit/api/) documents function inputs and outputs.

physioKIT requires Python 3.12–3.14. The quickstart uses synthetic ECG data, so it is the shortest way to check your installation before working with a recording.