semvec/pypi-packages huggingface dataset:
amazon-codeguru-jupyterlab-extension 1.1.1
Security, code quality, and ML recommendations
Requires Python: >=3.7

## Amazon CodeGuru JupyterLab Extension

Amazon CodeGuru extension for [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) and [SageMaker Studio](https://aws.amazon.com/sagemaker/studio/).
This extension runs scans on your notebook files and provides security recommendations and quality improvements to your code.

## Requirements

- JupyterLab >= 3.0

## Install

```sh
pip install amazon_codeguru_jupyterlab_extension
```

## Uninstall

```sh
pip uninstall amazon_codeguru_jupyterlab_extension
```

## Development

### Prerequisites

Ensure the following dependencies are available in your environment.

- Python >= 3.8
- JupyterLab >= 3.0
- NodeJS >= 18

Alternatively, you can create a conda virtual environment with the following commands:

```sh
conda env update --file binder/environment.yml
conda activate amazon-codeguru-extension-demo
```

### Manual Setup

1. Install the Python package in development mode.

```sh
pip install -e .
```

2. Link the extension with JupyterLab.

```sh
jupyter labextension develop . --overwrite
```

3. Build the Typescript source.

```sh
jlpm build
# or
jlpm watch # automatically rebuild changes
```

4. Start the JupyterLab server

```sh
jupyter lab
```

### Quick Setup

Run the following command to quickly build and install the extension.

```sh
python3 binder/postBuild
```

## Release

This extension can be distributed as a Python package.
First, install build dependencies:

```sh
pip install build twine hatch
```

Bump the version using `hatch`. By default this will create a tag.

```sh
hatch version <new-version>
```

To generate a new Python source package (`.tar.gz`) and the binary package (`.whl`) in the `dist/` directory, run the following command:

```sh
python -m build
```

Then to upload the package to PyPI, run the following command:

```sh
twine upload dist/*
```

## Security

See [SECURITY](SECURITY.md#security-issue-notifications) for more information.

## License

This project is licensed under the Apache-2.0 License.
PPTX2MD2REVEALJS 1.0.0
Converts a pptx file to MD File.
Requires Python: >=3.6

# pptx2md

`pptx2md` is a Python package that converts PowerPoint (.pptx) files into Markdown. It also supports converting speaker notes and outputting them in the Markdown file. The output Markdown file can be further converted into a Reveal.js presentation.

## Features

- Converts PowerPoint slides into Markdown
- Supports speaker notes
- Outputs speaker notes in the Markdown file
- Adds "---" at the end of each slide in the Markdown file
- Can be used to generate Reveal.js presentations

## Installation

You can install `pptx2md` with pip:

```bash
pip install pptx2md
MALDIpy 0.1.5
MALDI-MS data analysis at the single-cell level (Tutorials available at https://github.com/TheHumphreysLab/MALDIpy
Requires Python: >=3.6

Updates in MALDIpy v0.1.5; compared to v0.1.3:

New functions #1: MALDIpy.projection.add_coords;
#Extract X/Y coordinates from the adata.obs.index and added to obs

New functions #2: MALDIpy.featureplot.project_cluster_in_groups
#Plot any clusters in a group of samples (x/y coordinates should be in adata.obs for this function)
credoai-connect 0.1.3
Credo AI Connect: Integration point for Credo AI Governance Platform
Requires Python: >=3.7


--------------------------------------

# Connect by Credo AI - Utility Module for Connecting to AI Governance Platform

Connect is a utility module that integrates ML assessments with the Credo AI Governance Platform.
Connect defines `evidence_containers` which function as adapters between diverse AI assessment
frameworks (or custom analyses) and Credo AI-compatible `Evidence` objects.



## Dependencies

- Credo AI Connect supports Python 3.8+
- Sphinx (optional for local docs site)


## Installation

The latest stable release (and required dependencies) can be installed from PyPI.

```
pip install credoai-connect
```

## Documentation

Documentation is hosted by [readthedocs](https://credoai-connect.readthedocs.io/en/stable/).


## Logging
Logging defaults to stdout. In order to collect all logging information into a specific file,
set the desired location to the environment variable `CREDO_CONNECT_LOG_PATH`. 



> **Warning**
> Make sure the destination has write permissions, otherwise you will get a `PermissionError` at the moment you
> attempt library import.
immutable-builder 0.1.3
Immutable builder, carbon copy of lann/builder package in GoLang.
Requires Python: >=3.11,<4.0

# builder

https://pypi.org/project/immutable-builder/

Carbon copy of https://github.com/lann/builder for making a Python NoSQL query builder.

Installing:
```python
poetry add immutable-builder 
```

Example:

```python
from builder import Builder, get_struct, registry
from pprint import pprint
from typing import Optional

class UserBuilder(Builder):
        """
        The UserBuilder class inherits from Builder and provides specific methods to set the 'name' and 'age' attributes.
        """

        def __init__(self):
            super().__init__()

        def name(self, val: str) -> 'UserBuilder':
            """
            Creates a new UserBuilder with the 'name' attribute set to the provided value.
            """
            return self.set_value("name", val)

        def age(self, val: int) -> 'UserBuilder':
            """
            Creates a new UserBuilder with the 'age' attribute set to the provided value.
            """
            return self.set_value("age", val)


class User:
        """
        The User class represents the structure that the UserBuilder will build.
        """

        def __init__(self, name: Optional[str] = None, age: Optional[int] = None):
            self.name = name
            self.age = age


    # Register the immutable_builder-struct pair
registry.register(UserBuilder(), User())
user_builder = UserBuilder().name("caner").age(25).name("caner2")  # Build a user
user = get_struct(user_builder)  # Convert the immutable_builder to a struct
pprint(user.__dict__)  # Print the user struct's attributes

# Output:
# 
# {'age': 25, 'name': 'caner2'}
```

Thanks a lot to Lann for the original builder package, this is wonderful.

Not a perfect copy of the library, yet it works! Which is, what python is right? It is fast enough, and it works.
dnspropagation 0.0.4
DNS propagation checker
Requires Python: >=3.7

# dnspropagation

Simple CLI utility to check propagation of DNS records using multiple DNS servers.

## Installation
```
pip install dnspropagation
```

## Usage
In the most simple form you can just specify type of record and domain name:
```
dnspropagation a google.com

# or you can use docker version
docker run berkas1/dnspropagation a google.com
```

It will check entries for a given type and domain name using five default public nameservers. It returns a human-readable colorful table. You can use `--json` or `--yaml` parameters to make it machine-readable.

![Table 1](extras/table1.png)

### Custom DNS servers
You can either supply custom DNS server to query using the `--server` parameter, which can be used multiple times:
```shell
dnspropagation --server 1.1.1.1 --server 8.8.8.8 a google.com
# or
docker run berkas1/dnspropagation --server 1.1.1.1 --server 8.8.8.8 a google.com
```

OR you can add a yaml-formatted list of servers. This has to follow given format (as can be seen in [this](custom-list.yaml) file) and be supplied to the utility using the `--custom_list` parameter.


### Expected results
You can expect a value to be returned by DNS servers. If this value is returned, its color will be green. Otherwise the value will be shown red.
![expect1](extras/expect1.png)

### Filtering DNS servers
When using the default DNS servers or a custom list, you can limit which servers you want to query using parameters `--country` and `--owner`.
Both can be used multiple times. When used, the AND operation is used.

![owner](extras/owner1.png)