semvec/pypi-packages huggingface dataset:
pyprocessors-mazars_table 0.5.86
Sherpa Mazars normalizer
Requires Python: >=3.8

## Requirements

- Python 3.8+
- Flit to put Python packages and modules on PyPI
- Pydantic for the data parts.

## Installation
```
pip install flit
pip install pymultirole-plugins
```

## Publish the Python Package to PyPI
- Increment the version of your package in the `__init__.py` file:
```
"""An amazing package!"""

__version__ = 'x.y.z'
```
- Publish
```
flit publish
```
ggpt 0.2.0
Gorgeous CLI tools using GPT
Requires Python: >=3.7.1,<4.0.0

# ggpt

##### Gorgeous CLI tools using GPT.

<br/>
<br/>

## Installation


```
pip install ggpt
```

<br/>
<br/>

## Setup
1. Obtain an OpenAI API key at https://beta.openai.com/account/api-keys

2. Add it to your shell environment variables
	```
	export OPENAI_API_KEY=<YOUR_API_KEY_HERE>
	```

<br/>
<br/>

## Usage

<br/>

###  docstring

###### generates automated docstring based on code changes.


<br/> 

```
ggpt docstring [OPTIONS]
```

<br/>

| Option         | Description                                       |
| -------------- | ------------------------------------------------- |
| `--api-key TEXT`|OpenAI API Key. <br/> If not provided, `OPENAI_API_KEY` environment variable is used. |
| `--path PATH`   | Path to the Git repository to search for code changes. <br/> If not provided, the current directory is used.|
| `--hash TEXT`   | Hash of the commit to review. <br/>  If not provided, unstaged changes are reviewed by default.     |
| `--staged`      | Include only staged changes in the review. <br/> If not provided, unstaged changes are reviewed by default.  |

<br/>

https://user-images.githubusercontent.com/123562684/227761450-9297f709-3d61-448c-ad78-a04f9a5f41d8.mov

<br/>

---

<br/>


###  review

###### generates automated code review based on code changes.


<br/>

```
ggpt review [OPTIONS]
```

<br/>

| Option         | Description                                       |
| -------------- | ------------------------------------------------- |
| `--api-key TEXT`|OpenAI API Key. <br/> If not provided, `OPENAI_API_KEY` environment variable is used. |
| `--path PATH`   | Path to the Git repository to search for code changes. <br/> If not provided, the current directory is used.|
| `--hash TEXT`   | Hash of the commit to review. <br/>  If not provided, unstaged changes are reviewed by default.     |
| `--staged`      | Include only staged changes in the review. <br/> If not provided, unstaged changes are reviewed by default.  |

<br/>

https://user-images.githubusercontent.com/123562684/227762097-1c42b186-014f-48fe-b116-ee5e6c39f9f7.mov

<br/>

---

<br/>


###  naming

###### suggests variable names based on submitted prompt. 


<br/>

```
ggpt naming PROMPT [OPTIONS]
```

<br/>

| Option         | Description                                       |
| -------------- | ------------------------------------------------- |
| `--api-key TEXT`|OpenAI API Key. <br/> If not provided, `OPENAI_API_KEY` environment variable is used. |

<br/>

https://user-images.githubusercontent.com/123562684/227763626-d329f156-c7f6-4a46-bfee-57504882d62c.mov


<br/>

## License


MIT LISENCE
Priyal-102103274 0.2
A python package for implementing topsis

# Topsis-Priyal-102103274

For : Assignment(UCS654)<br>
Submitted by: Priyal Singla<br>
Roll no:102103274
Group:3COE10

## Description

This is a python package used to implement TOPSIS(Technique of Order Preference Similarity to the Ideal Solution) for MCDA(Multiple criteria decision analysis)

<br>

## How to use this package:

## Installation

pip install Topsis-Priyal-102103274

## Example:

## Sample dataset

| Fund Name | P1   | P2   | P3  | P4   | P5    |
| --------- | ---- | ---- | --- | ---- | ----- |
| M1        | 0.78 | 0.61 | 5.5 | 34.7 | 10.4  |
| M2        | 0.88 | 0.77 | 5   | 58.4 | 16.26 |
| M3        | 0.61 | 0.37 | 5.9 | 39.9 | 11.7  |
| M4        | 0.76 | 0.58 | 4.2 | 57.7 | 15.81 |
| M5        | 0.84 | 0.71 | 3.2 | 48   | 13.19 |
| M6        | 0.76 | 0.58 | 4   | 68.8 | 18.54 |
| M7        | 0.81 | 0.66 | 6.5 | 38.2 | 11.54 |
| M8        | 0.81 | 0.66 | 3.2 | 32.8 | 9.37  |

## Input

### In Command Prompt

Enter filename followed by .csv or .xlsx extension, then enter values of weights separated by commas like "1,1,1,2,2",then enter values of impacts separated by commas like "+,+,-,-,+" without giving space in between comma value, then enter name of file where you want to save output followed by .csv extension

```
python -m Topsis_Priyal-102103274 data.xlsx "1,1,1,2,2" "+,+,-,-,+" output.csv
```

## Output

This will be in our Output csv file
Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------ | ------------ | ------------
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4 | 0.5303740545041122 | 4
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26 | 0.5372510220778413 | 3
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7 | 0.4715707210914604 | 8
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81 | 0.5099483054760279 | 6
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19 | 0.57723478293325 | 1
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54 | 0.49447887833737925 | 7
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54 | 0.5244107252631429 | 5
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37 | 0.5576533672285703 | 2
wagtail-rss-feed-block 0.1.0
Content block displaying RSS feed for Wagtail CMS
Requires Python: >=3.7


# Wagtail RSS feed

Wagtail RSS feed is a Django app that provides custom content block that can be used with any Wagtail page model.

This way you can load any RSS feed and display it on a any Wagtail page.

## Quick start

1. Install "wagtail_rss_feed_block" using pip
    
    ```
    $ pip install wagtail-rss-feed-block
    ```

2. Add "wagtail_rss_feed_block" to your INSTALLED_APPS setting like this:

    ```
    INSTALLED_APPS = [
        ...,
        "wagtail_rss_feed_block",
    ]
    ```

3. Use RSSFeedBlock in your Wagtail Page model for example like this:

    ```
    from wagtail.models import Page
    from wagtail.fields import StreamField
    from wagtail_rss_feed_block.blocks import RSSFeedBlock

    class BlogPage(Page):
        body = StreamField([
            ...
            ('rss_feed', RSSFeedBlock()),
            ...
        ])

        content_panels = [       
            FieldPanel('body'),
        ]
    ```

4. Make and apply database migrations with new changes:

    ```
    $ python manage.py makemigrations
    $ python manage.py migrate
    ```

## Development

1. Clone this repo:
    
    ```
    $ git clone git@github.com:LESPROJEKT/wagtail-rss-feed-block.git
    ```

2. Use pip to install this package from newly cloned repo folder to your local Django project ( -e argument stands for --editable and it's handy for local development. It will reload your Django project every time you make changes to wagtail-rss-feed-block.):
    
    ```
    $ pip install -e <path to repo folder>
    ```

3. Now you can continue from step no. 2 of Quick start guide above.