https://github.com/hasanisaeed/recurrence-plot-py

An advanced technique of nonlinear data analysis
https://github.com/hasanisaeed/recurrence-plot-py

cnn convolutional-neural-networks recurrence recurrence-plot time-series time-series-analysis timeseries

Last synced: 11 months ago
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An advanced technique of nonlinear data analysis

• Host: GitHub
• URL: https://github.com/hasanisaeed/recurrence-plot-py
• Owner: hasanisaeed
• License: other
• Created: 2022-02-24T18:38:09.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2024-11-15T16:25:44.000Z (over 1 year ago)
• Last Synced: 2024-11-15T16:33:22.835Z (over 1 year ago)
• Topics: cnn, convolutional-neural-networks, recurrence, recurrence-plot, time-series, time-series-analysis, timeseries
• Language: Python
• Homepage:
• Size: 524 KB
• Stars: 7
• Watchers: 1
• Forks: 1
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Recurrence Plot

[Recurrence Plot](https://en.wikipedia.org/wiki/Recurrence_plot) – A recurrence plot (RP) is an advanced technique of **nonlinear** data analysis. It is a visualisation (or a graph) of a square matrix, in which the matrix elements correspond to those times at which a state of a dynamical system recurs (columns and rows correspond then to a certain pair of times).

## Result

![](results/1D_to_2D.jpg)

## Usage

**1. Install requirements:**

Ensure you have Python 3 installed. Install the required dependencies using:

    pip install -r requirements.txt

**2. Run `example.py` script:**

    python3 example.py # or python example.py

This will:

- Generate a random signal.

- Smooth the signal using a moving average filter.

- Compute and visualize the recurrence plot.

- Save the resulting plot as results/1D_to_2D.jpg.

----

## How to Use the recurrence Package

If you want to use the recurrence package in your own projects:

1) **Import the Required Modules:**

    For recurrence plot functions, import from recurrence.plotting.

    For signal processing utilities, import from recurrence.convolve.

    Example:

    ```python

    from recurrence.plotting import setup_plot, save_plot

    from recurrence.convolve import calculate_convolve

    ```

2) **Process Your Signal:** 

   

   Use `calculate_convolve` to smooth your input signal, then use the `setup_plot` and `save_plot` functions to generate and save recurrence plots.