PCEA 0.1.1

A python package for compound event analysis.

PCEA

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What is PCEA?

pyCEA (Python Compound Event Analysis) identifies events in signals using specific thresholds and computes their occurrence time, intensity, peak, and duration. It also detects event chains (compound events) and calculates their probabilities, such as:

• ENSO-drought-wildfire chain events
• Heatwave-drought compound events
• Drought-flood compound events

Compound events are the superposition of events within a specific time window, exhibiting six types of relationships.

Dependencies

The following Python packages are required:

• numpy <https://numpy.org/>_
• pandas <https://pandas.pydata.org/>_

Installation

Install pyCEA via pip::

pip install PCEA

Usage

Here are two quick examples of using pyCEA:

1. Reading data from a CSV file::

   import numpy as np from PCEA import CEA import pandas as pd

   Read data from demo.csv, where columns represent variables and rows represent sampling times.

   Ensure that demo.csv exists and has numerical data.

   ts = pd.read_csv("./data/demo.csv", index_col=0, header=0)

   Select specific columns for analysis

   ts = ts.iloc[:, [0, 1]]

   Create a CEA instance with specified parameters

   cea = CEA(ts, delta=6, threshold=[-np.inf, -0.5])

   Run compound event analysis and save the results to an Excel file

   cea.run_cea(save_path='./data/results.xlsx')

2. Input a boolean matrix::

   import numpy as np from PCEA import CEA

   Generate a boolean matrix (720 rows by 3 columns)

   ts = np.random.choice([True, False], [720, 3])

   Create a CEA instance for boolean input, setting is_binary_array to True

   cea = CEA(ts, delta=3, is_binary_array=True)

   Run compound event analysis and save the results to an Excel file

   cea.run_cea(save_path='./data/results.xlsx')

More examples can be found in Github <https://github.com/Koni2020/PCEA/blob/master/README.md>_.

References

• Donges J F, Schleussner C F, Siegmund J F, et al. Event coincidence analysis for quantifying statistical interrelationships between event time series: On the role of flood events as triggers of epidemic outbreaks[J]. The European Physical Journal Special Topics, 2016, 225: 471-487. <https://link.springer.com/article/10.1140/epjst/e2015-50233-y>_