semvec/pypi-packages huggingface dataset:
morrisseau-cleaner 0.11.1
A Python library to clean data for Morrisseau Project
Requires Python: >=3.6

morrisseau-cleaner
==================

.. image:: https://img.shields.io/pypi/v/morrisseau-cleaner
   :target: https://pypi.org/project/morrisseau-cleaner/
   :alt: PyPI - Version

A Python library to clean data for the Morrisseau Project

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

For macOS, you may need to install Tkinter first:

.. code-block:: bash

   brew install python-tk

For Ubuntu, you may need to install Tkinter first:

.. code-block:: bash

   sudo apt-get install python3-tk

Then install the package:


.. code-block:: bash

   pip install morrisseau-cleaner

Upgrade
-------

.. code-block:: bash

   pip install --upgrade morrisseau-cleaner
pi-promptflow-tools 3.0.1
A tool package for the ProcessInsights team to us for custom prompt flow tools.

## Version History

- 2.0.0: Initial release version.
- 2.1.0: Removed 100 word limit from metaprompt.
- 2.2.0: Removed instructions for ChatGpt to decide if the answer is sufficient or not. Added instructions for ChatGpt to not talk about process mining capabilities from any MSFT competitor.
- 3.0.0: Added input for Record Count.
- 3.0.1: Fix for Yaml
calliope-music 10.0
Toolkit for working with playlists of music.

```
  oooooooo8             o888  o888  o88
o888     88   ooooooo    888   888  oooo   ooooooo  ooooooooo    ooooooooo8
888           ooooo888   888   888   888 888     888 888    888 888oooooo8
888o     oo 888    888   888   888   888 888     888 888    888 888
 888oooo88   88ooo88 8o o888o o888o o888o  88ooo88   888ooo88     88oooo888
                                                    o888
```

Calliope is a toolkit for working with playlists of music.

The goal is to enable research and experimentation in open source music
recommendation, by providing a set of standard interfaces and operations
to online and local music services.

In order words, a way to mash up data from Spotify, Musicbrainz, Last.fm
and your local music collection!

For more information, see the manual:
<https://calliope-music.readthedocs.io/en/latest/>
wncfe-query-inspector 1.0.1
A Django middleware package to print SQL queries to the terminal
Requires Python: >=3.12

SQL Tools
=================
Simple middleware tool for analyzing Django ORM SQL execution. 

# Usage
Add the follow line of code to your middleware in your project settings.py file
```
'wncfe-sql-tools.middleware.new_middleware'
```

# How it works
The tool uses Django built in features to inspect the SQL generated from queries executed by the application. 3rd party tools are used to format and highlight the SQL presented in the terminal window.
MOVE3 0.7
Maintence of Variance Toolbox
Requires Python: >3.9

[![DOI](https://zenodo.org/badge/392819995.svg)](https://zenodo.org/badge/latestdoi/392819995)
## 

Installation

```python
pip install MOVE3
```

## MOVE1

MOVE1 code reads directly from a HEC-DSS file. 

https://github.com/danhamill/MOVE3/blob/2fce4a71c0758fc276b809f313bb04b2ed53c2ae/move3/test/test.py#L39-L68

Code is tested against an [example](https://www.hec.usace.army.mil/confluence/display/SSPTutorialsGuides/Daily+Flow+Record+Extension+with+MOVE.1) provided by the USACE Hydrologic Engineering Center.

## MOVE3
MOVE3 code read directly from a text file. 

Code is tested aginst an [example](https://pubs.er.usgs.gov/publication/tm4B5) from Appendix 8 in Bulletin 17C.

Data are read from a csv format with the following format:

| WY   | flow |
|------|------|
| 1900 | 3000 |
| 1901 | 5000 |

There should be a individual files for short and long records.  The files get merged into a single dataframe within the code. Example input is provided in `data/`

https://github.com/danhamill/MOVE3/blob/2fce4a71c0758fc276b809f313bb04b2ed53c2ae/move3/test/test.py#L88-L95

This application is designed to perform Bulletin 17C (England et al. 2019) record extension using MOVE.3 and MOVE.1 Methodologies.  

There are three possible extension using the MOVE.3:
1. Full N2 extension - This provides the longest possible short record extension.  Be careful because this type of extension has the potential to be artificially long and could result in underestimation of uncertainty of the resulting flood frequency curve.
2. ne (mean) extension - This provides extension based upon the variance of the mean.  
3. ne (variance extension) - This provides the shortest possible short record extension using the variance of variance.  This type of extension is preferable when uncertainty is primary concern.  Note: This is the preferred approach presented in Bulletin 17C

### References
- England, John F., Jr., Timothy A. Cohn, Beth A. Faber, Jery R. Stedinger, Wilbert O. Thomas Jr., Andrea G. Veilleux, Julie E. Kiang, and Robert R. Mason, Jr. 2019. “Guidelines for Determining Flood Flow Frequency—Bulletin 17C.” Techniques and Methods. US Geological Survey. https://doi.org/10.3133/tm4b5.

### Requirements

Tested on python 3.9

1. altair
2. sklearn
3. numpy
4. pandas
5. streamlit

### To run app

Working dashboard for MOVE.3 streamflow record extension.

```
streamlit run move3\ui_move.py
```

## To Test Algorthims
```
pytest -v move3\test\test.py
```