semvec/pypi-packages huggingface dataset:
PCEA 0.1.1
A python package for compound event analysis.
License: MIT

PCEA
=====

.. image:: https://img.shields.io/badge/Host-pyCEA%2FREADME-orange
   :target: https://github.com/Koni2020/pyCEA/blob/master/README.md

.. image:: https://img.shields.io/badge/Python-3.10-blue

.. image:: https://img.shields.io/badge/Status-Building-green


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>`_
idgeneratorha 0.0.10
An id generator that generated various types and lengths ids
License: MIT
Requires Python: >=3.10

# ID Generator
A package used to generate random ids of variable lengths and types.

## How to use
_After installing the package use following import:_ <br>

**from idgenerator import generate_password,generate_guid,generate_credit_card_number,
    generate_pin_number,
    generate_object_id**

_Then use following commands:_

**password = generate_password(length= 'your_desired_length')<br>**

**guid = generate_guid()<br>**

**credit_card_number = generate_credit_card_number(length='your_desired_length') <br>**

**pin = generate_pin_number(length='your_desired_length') <br>**

**objid = generate_object_id() <br>**
ecsm-tool 0.0.6
ECSM TOOLS
License: MIT
Keywords: ECSM, TOOLS, API


# ECSM-TOOL

ECSM 指令完全符合 ECSM APIv1 规范，暂时支持如下功能，后续持续迭代以增加更多功能子集。

- ECSM_TOOLS
    - images
        - list [default, id, info]
        - upload
        - delete
        - download [just for test]
        - summary