semvec/pypi-packages huggingface dataset:
esgriskmon-climaterisk 0.0.1
Package for Climate Risk Analytics.
Requires Python: >=3.9
Keywords: climate,risk

# CLIMATE RISK ANALYTICS PACKAGE


This package provides the following analytics which can be run end-to-end (retrieval to analytics output):
- carbon footprint (weighted average carbon intensity - WACI; financed emissions - TCE; owned emissions - OE) for the equity portfolio and benchmark and ftse all cap.
- share of equity nav which falls in each MSCI low carbon transition score
- implied temperature rise breakdown by sector and portfolio/benchmark total
- attribution of WACI for portfolio t vs portfolio t-1
- attribution of WACI for portfolio vs benchmark
- quality score
- country-sector change in data quality
- country-sector concentrations


#### PLEASE NOTE: if something looks like a bug, walks like a bug and calls(?) like a bug, it's probably a bug. Please contact Ginni to fix/discuss




## Structure, how to install, how to run:
This package provides 
- storages (getting data and validating)
- services (making calculations / analytics)


#### How to install: pip install git+https://github.com/NBIM/climate_risk.git
This will pick up every time a version is incremented.
#### How to run: All of these are united in the views folder. In order to run the analytics the user needs to run the scripts in the views, not the other files.
You can also use the services autonomously.


## Important notes on how to use this package:
- to run most analytics the user will need access to the underlying schemas and tables used.
- make sure to change the credentials to your snowflake account (email needed)
- scripts will run for specific portfolios (ftse all cap/eq portfolio/eq benchmark); specific fk_dim_date and specific ICB level. Make sure to change the variables at the beginning of the script to change any of these parameters.





## Future improvements:
- tests!
- remove storage from this package, keep only services. Or at least separate the two.
- generalise settings/configurations for constants and authorisations
- improve validations upon data retrieval


#### published following https://python-packaging.readthedocs.io/en/latest/minimal.html
cofunctions 2023.2.1
cofunction: simple coroutines
Requires Python: >=3.6
Keywords: generators,coroutines

cofunctions: Simple coroutines for Python
=========================================

To create a cofunction, write a generator with the ``@cofunction`` decorator
and ``yield`` a function that does your processing.

Example of a function that accumulates a sum:

```py
from cofunctions import cofunction
from math import exp, log

@cofunction
def logsumexp(initial=0.):
    '''log-sum-exp example (does not actually prevent overflow)'''
    sum = initial

    # this function gathers the inputs
    def _(x):
        nonlocal sum  # important for changing values
        sum += exp(x)
        return sum

    yield _

    # do additional processing here

    result = log(sum)

    return result

# create the cofunction
lse = logsumexp()

# call the cofunction to do processing
partial1 = lse(1)
partial2 = lse(2)
partial3 = lse(3)

# finish the cofunction to run the additional processing
result = lse.finish()

print(partial1, partial2, partial3)
# 2.718281828459045 10.107337927389695 30.19287485057736
print(result)
# 3.40760596444438
```
mediadata 1.0.7
A wrapper tool for mediainfo tool

## A simple wrapper for mediainfo tool available as Global PYPI Python Package

### For Python

Pip Install :

```
pip install mediadata
```
```
Package Link: https://pypi.org/project/mediadata/1.0.7/
```
### Prerequisites :

This tool needs&nbsp;<mark>mediainfo</mark>&nbsp;to be installed inorder to work properly.

Soon available on Ubuntu/Debian:

```
sudo apt install mediainfo
sudo apt install mediadata
```

#### Usage

To get all available info about a video file. It works with all media types.

```
mediadata --file="some video.mp4" track --type={General/Audio/Video} --list-keys

```

To get info about one particular key:

```
mediadata --file="some video.mp4" track --type={General/Audio/Video} --key=Duration

```

#### Build Deb

```
python setup.py --command-packages=stdeb.command bdist_deb
```

<!-- #### Install Deb

```
sudo apt install python3-pymongo python3-gridfs python3-pymongo-ext python3-bson
sudo dpkg -i python3-mediadata_0.1.5-1_all.deb
``` -->
perceptron-pkg-deftydev 0.0.5
A small package for perceptron
Requires Python: >=3.6

# perceptron_package


References -
https://packaging.python.org/en/latest/tutorials/packaging-projects/


pypi package successfully posted- pip install perceptron-pkg-deftydev==0.0.3

![image](https://user-images.githubusercontent.com/58273709/226369103-2927cc44-d403-4abf-a010-ccd101cd9c46.png)
climate-risk 0.0.2
Package for Climate Risk Analytics.

# CLIMATE RISK ANALYTICS PACKAGE


This package provides the following analytics which can be run end-to-end (retrieval to analytics output):
- carbon footprint (weighted average carbon intensity - WACI; financed emissions - TCE; owned emissions - OE) for the equity portfolio and benchmark and ftse all cap.
- share of equity nav which falls in each MSCI low carbon transition score
- implied temperature rise breakdown by sector and portfolio/benchmark total
- attribution of WACI for portfolio t vs portfolio t-1
- attribution of WACI for portfolio vs benchmark
- quality score
- country-sector change in data quality
- country-sector concentrations


#### PLEASE NOTE: if something looks like a bug, walks like a bug and calls(?) like a bug, it's probably a bug. Please contact Ginni to fix/discuss




## Structure, how to install, how to run:
This package provides 
- storages (getting data and validating)
- services (making calculations / analytics)


#### How to install: pip install git+https://github.com/NBIM/climate_risk.git
This will pick up every time a version is incremented.
#### How to run: All of these are united in the views folder. In order to run the analytics the user needs to run the scripts in the views, not the other files.
You can also use the services autonomously.


## Important notes on how to use this package:
- to run most analytics the user will need access to the underlying schemas and tables used.
- make sure to change the credentials to your snowflake account (email needed)
- scripts will run for specific portfolios (ftse all cap/eq portfolio/eq benchmark); specific fk_dim_date and specific ICB level. Make sure to change the variables at the beginning of the script to change any of these parameters.





## Future improvements:
- tests!
- remove storage from this package, keep only services. Or at least separate the two.
- generalise settings/configurations for constants and authorisations
- improve validations upon data retrieval


#### published following https://python-packaging.readthedocs.io/en/latest/minimal.html
openoc 0.1.4
free optimal control software
Requires Python: >=3.9

openoc: free optimal control software
=====================================

Introduction
------------
**Openoc** is a Python package that solves optimal control problems numerically. It utilizes advanced techniques to be powerful, easy to use, and fast.

- **Powerful**: Openoc can solve basically any optimal problem. It employs a multi-phase optimal control model which allows continuous/non-continuous state and control variables, path/integral/boundary constraints, and fixed/free initial and terminal time.

- **Easy to use**: Openoc is designed to be easy to use. It provides a SymPy_-based, intuitive interface for defining and solving problems.

- **Fast**: Openoc is fast. It uses various techniques to speed up the entire compilation & solution process, including symbolic differentiation (with SymPy_), JIT compilation (with Numba_), meta-programming, and more.

Installation
------------
The easiest way to install openoc is using conda_:

1. Install Anaconda_ and create a new environment with **Python 3.9.***

2. Activate the environment, and run in the terminal:

   .. code-block:: console

       conda install numpy scipy sympy numba
       conda install -c conda-forge cyipopt
       pip install openoc

License
-------
MIT. Feel safe to use it in your research & projects.

.. _SymPy: https://www.sympy.org/
.. _Numba: https://numba.pydata.org/
.. _conda: https://conda.io/
.. _Anaconda: https://www.anaconda.com/
mwclient-contenttranslation 0.1.1.post1
Wikipedia ContentTranslation (CXServer) API wrapper to be used with mwclient
Requires Python: >=3.9,<4.0

[![PyPI version](https://badge.fury.io/py/mwclient-contenttranslation.svg)](https://badge.fury.io/py/mwclient-contenttranslation)

# mwclient-contenttranslation
The wrapper of ContentTranslation (CXServer) API of Wikipedia to be used with mwclient.

## Usage
Install via PyPI: `pip install mwclient-contenttranslation`
```python
from mwclient import Site
from mwclient_contenttranslation import CxTranslator
site = Site("en.wikipedia.org")
site.login(USERNAME, PASSWORD)
translator = CxTranslator(site)
translator.translate_text(["小熊維尼", "蜂蜜罐", "小熊維尼掉進蜂蜜罐"])
# ['Winnie the Pooh', 'honey pot', 'Winnie the Pooh fell into a jar of honey']
```

## Disclaimer
Intended for good use only. Never abuse it.
rough-set 0.1.0
A Python library that provides a set of tools to calculate rough sets and obtain reduct rules.

# 🐍 Rough Set Python Package

Rough Set Python Package is a Python library that provides a set of tools to calculate rough sets and obtain reduct rules. This package is useful for data analysis and machine learning tasks where you need to deal with uncertainty or incomplete data.

## 📦 Installation
You can install Rough Set Python Package using pip command:

```bash
pip install roughset
```

## 📚 Usage
With a example data
![Example data](https://i.imgur.com/AHzxjiu.png)

```python
from roughset.reduct import create_reduct_rules

# Load data from a CSV file
df = pd.read_csv('example.csv')

# Calculate reduct rules
create_reduct_rules(
    df=df,
    name_col="No",
    feature_col=['天氣', '事故情形', '事故原因'],
    decision_col='損壞部位',
    include_empty=True # Include empty reduct rules
)
```
We will get the reduct rules.
![reduct rules result](https://i.imgur.com/wyG1wUr.png)

## 🤝 Contribution
If you want to contribute to Rough Set Python Package, you can fork the repository on GitHub and create a pull request. You can also report bugs, suggest new features, or ask for help in the issues section.

## 📜 License

Rough Set Python Package is released under the MIT License. You can find the details of the license in the `LICENSE` file.
idataengineer 0.3.0
innovata-DataEngineer
Requires Python: >=3.8

# DataEngineer

MongoDB 를 기반으로 하는 모든 프로젝트를 위한 Database-Package.


<!-- ============================================================ -->
## 가상환경구성
https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/#creating-a-virtual-environment

1. Windows
py -m venv env
.\env\Scripts\activate
where python



<!-- ============================================================ -->
## PyPi 패키지 업로드
https://docs.python.org/3/distributing/index.html


python -m pip install setuptools wheel twine
py -m pip install --upgrade build
py -m build