semvec/pypi-packages huggingface dataset:
flask-os 0.2.24022223
flask项目基础一键生成
Requires Python: >=3.4

flask-os
========

Documentation
-------------

The documentation is hosted at https://github.com/BingerYang/flask-os


Installation
------------

.. code:: shell

     pip install flask-os

Usage
-----

example:

:: shell
  flask-admin project drill
  flask-admin app scene --project drill
rdanalyzer 0.0.3
A small example package
Requires Python: >=3.7

# RDAnalyzer
Reactive Diffusion Analyzer (RDAnalyzer)

RDAnalyzer is a Python module for analyzing ReaxFF MD trajectory, especially the hydroxide diffusion in AEM.

Features:
- Splitting trajectory according to user's setting
- Transforming text trajectory to Atoms, Molecules, and Reactions
- Analysis based on these Atoms, Molecules, and Reactions
- Calculation of drift length of hydroxide for obtaining conductivity


## Installation
RDAnalyzer is developed on Ubuntu OS with python 3.11

RDAnalyzer needs `scipy`, `numpy`, `matplotlib`, `ase` and `networkx`,
but you always do not need install them manually when using pip to install.

**Install with pip**

```pip install -i https://test.pypi.org/simple/ RDAnalyzer-test1==0.0.1```

## Getting started
See `tests/Tutorial.ipynb` for detailed usage about RDAnalyzer 

## References
- ref
PyFolioC 0.1
Portfolio Optimization Package

# PyFolioC


The PyFolioCC class is designed to build an optimal portfolio in the sense of Markowitz using general graph clustering 
techniques. The idea is to provide a historical return database of an asset universe (historical_data), a lookback window 
(lookback_window) for portfolio construction, a number of clusters (number_clusters), a clustering method (clustering_method), 
and an evaluation window (evaluation_window). From there, the objective is to construct a portfolio based on historical return 
data over the period corresponding to lookback_window by creating a sub-portfolio composed of a specified number of synthetic 
assets (ETFs) using the clustering method specified in clustering_method. The performance (Sharpe ratio and cumulative PnL) of
the constructed portfolio is then evaluated over the evaluation_window.