semvec/pypi-packages huggingface dataset:
iceberg-diag 0.1.5
Upsolver Iceberg Auditor CLI
License: MIT
Requires Python: <4.0,>=3.8

# Iceberg Diagnostics Tool

## Overview

The Iceberg Table Analysis CLI Tool evaluates Iceberg tables to identify how Upsolver optimizations can enhance efficiency.   
It presents a side-by-side comparison of current metrics against potential improvements in scan durations, file counts, and file sizes, providing a straightforward assessment of optimization opportunities.


## Installation
`iceberg-diag` can be installed using either Brew or PIP, as detailed below:

### Using PIP
#### Prerequisites

* **Python 3.8 or higher**: Verify Python's installation:
   ```bash
   python3 --version
   ```
To install `iceberg-diag` using PIP, ensure you have the latest version of `pip`:

```bash
pip install --upgrade pip
```
Then, install the package with `pip`
```bash
pip install iceberg-diag
```


### Using Brew
Execute the following commands to install `iceberg-diag` via Brew:

 ```bash
 brew tap upsolver/iceberg-diag
 brew install iceberg-diag
 ```


## Usage Instructions

```bash
iceberg-diag [options]
```

### Command-Line Options

- `-h`, `--help`: Display the help message and exit.
- `--profile PROFILE`: Set the AWS credentials profile for the session, defaults to the environment's current settings.
- `--region REGION`: Set the AWS region for operations, defaults to the specified profile's default region.
- `--database DATABASE`: Set the database name, will list all available iceberg tables if no `--table-name` provided.
- `--table-name TABLE_NAME`: Enter the table name or a glob pattern (e.g., `'*'`, `'tbl_*'`).
- `--remote`: Enable remote diagnostics by sending data to the Upsolver API for processing.   
Provides more detailed analytics and includes information about file size reducations.
- `-v, --verbose`: Enable verbose logging

### Usage
1. Displaying help information:
    ```bash
     iceberg-diag --help
    ```
   
2. Listing all available databases in profile:
    ```bash
   iceberg-diag --profile <profile>
    ```
   
3. Listing all available iceberg tables in a given database:
    ```bash
   iceberg-diag --profile <profile> --database <database>
    ```
4. Running diagnostics on a specific table in a specific AWS profile and region (completely locally):
   ```bash
    iceberg-diag --profile <profile> --region <region> --database <database> --table-name '*'
    ```
   
5. Running diagnostics using `remote` option
    ```bash
   iceberg-diag --profile <profile> --database <database> --table-name 'prod_*' --remote
    ```
timeflux 0.17.2
Acquisition and real-time processing of biosignals
License: MIT
Keywords: time series, machine learning, biosignals, brain-computer interface, BCI, EEG, ECG, PPG, EMG, EDA, GSR

|Build| |Doc| |PyPI| |Contributor Covenant|

|Logo|

Timeflux
========

Timeflux is a free and open-source framework for acquiring and processing biosignals in real-time.

Use it to bootstrap your research, build brain-computer interfaces,
closed-loop biofeedback applications, interactive installations, and
more. Written in Python, it works on Linux, MacOS and Windows. Made for
researchers and hackers alike.

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

You can easily install the latest stable release from PyPI. We higlhy recommend that you use an environment management system such as `Conda <https://docs.conda.io/en/latest/miniconda.html>`__ to keep everything tidy.

::

    $ conda create --name timeflux python=3.10 pytables
    $ conda activate timeflux
    $ pip install timeflux

Getting started
---------------

`Read the documentation <https://doc.timeflux.io>`__.

.. |Build| image:: https://img.shields.io/github/actions/workflow/status/timeflux/timeflux/build.yml?branch=master
.. |Doc| image:: https://img.shields.io/readthedocs/timeflux/latest
.. |PyPI| image:: https://img.shields.io/pypi/v/timeflux
.. |Contributor Covenant| image:: https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg
.. |Logo| image:: https://avatars3.githubusercontent.com/u/44117793?v=4&s=100
gendummy 0.1.1
A library for generating dummy data in pandas DataFrames.
License: MIT
Requires Python: >=3.6

# Gendummy

**Gendummy** is a Python library for generating dummy data in the form of pandas DataFrames or xlsx spreadsheets. It provides functionality to create both automatic and manually specified data tables. 

## Features

- Generate DataFrames with random data, where dtyps are defined automatically (col[0] is always str).
- Generate DataFrames with random data, where dtyps are defined manually.


## Installation

To use the Gendummy library, ensure you have Python and pip installed, then install the required package:

```bash
    pip install gendummy
```

## Import

```python
    import gendummy as gd
```

## Usage

```python
    df = gd.gendf_auto(cols=3, rows=3)
    df = gd.gendf_manual(cols=3, rows=3, dtypes=[str, float, str])
    gd.genxls_auto(cols=3, rows=3)
    gd.genxls_manual(cols=3, rows=3, dtypes=[str, float, str])

```
jwt-kms 0.1.4
Library to offload some JWT crypto operations to KMS
License: MIT
Requires Python: >=3.9.2

# Python `jwt_kms` library

This library is work in progress.

Isolating private asymmetric keys to AWS KMS helps improve security by 
making it next to impossible to make copies of them. This library aims to 
provide a simple interface to use KMS keys to sign payloads into JWS tokens 
and/or to encrypt payloads into JWE tokens.

Signing with RSA and EC keys is currently supported.

## Keys

```
import boto3
from jwt_kms import jwk

client = boto3.client('kms')
key = jwk.JWK(client, 'some-key-id')

public_key_pem = key.public_key_pem
```

## Signing

```
from jwt_kms import jws

payload = {
   'something': 'yes',
   'more_something': 'abc'
}

token = jws.JWS(payload).add_signature(key, 'RS256').serialize(compact=True)  # or compact=False
```

## Encrypting

TODO.