semvec/pypi-packages huggingface dataset:
ALMAFE-ConfigDelivery 0.0.2
Classes for retrieval and transformation of ALMA Front End subassembly configuration data
Requires Python: >=3.6

# ALMAFE-ConfigDelivery package
Classes for retrieval and transformation of ALMA Front End subassembly configuration data

## ConfigDelivery.CCA6
### CCA6Database
Access/update the ALMA band 6 test data database
### DataDelivery, DeliveryConfig, ConfigFiles
Collect CCA6 measurement results and operating parameters in the required format for delivery to ALMA.

## ConfigDelivery.FEMC
Class for creating the required INI files for the FEMC module from the XML data delivery packages and/or TMCDB XML files.
Work in progress.

## ConfigDelivery.Plot.WCA
Produce plots required for WCA config delivery

## ConfigDelivery.WCA
### WCADatabase
Access/update the WCA tables in the FETMS database and produce WCA config delivery files
### WCADatafiles
Access the WCA test data files storage area under \\CVFiler\LO
### WCAOutputPower
Access/update the WCA output power tables int he FETMS database

## FETMSDatabase
### TestDataHeader
Access/update test data header records in the FETMS database.
dataset-cli 1.1.1
Dataset Generate CLI
Requires Python: >=3.8,<3.9

[![Python 3.8](https://img.shields.io/badge/python-3.8-blue.svg)](https://www.python.org/downloads/release/python-386/)
[![Poetry 1.0.10](https://img.shields.io/badge/poetry-1.0.10-blue.svg)](https://github.com/sdispater/poetry/releases/tag/1.0.10)

# dataset-cli

## Getting started

This project uses:

- [poetry](https://python-poetry.org/) as dependency manager
- [mypy](http://mypy-lang.org/) for static typing analysis
- [black](https://black.readthedocs.io/) for code formatting
- [flake8](http://flake8.pycqa.org/) for linting
- [pytest](https://docs.pytest.org/) as testing framework
- [coverage](https://coverage.readthedocs.io/) for code coverage reporting
- [pre-commit](https://pre-commit.com/) to setup git hooks (formatting and linting before commits)

```sh
# Clone the repository
git clone git@bitbucket.org:newtralmedia/dataset-cli-lib

# Install the dependencies using poetry
poetry install

# Enable virtual environment
poetry shell

# Setup pre commit hooks
poetry run pre-commit install
poetry run pre-commit run --all-files

# If you need create a tunel ssh to connect to remote mongo
ssh -i key.pem user@remotehost -L 27017:localhost:27017 -N
```

## Cli Commands

```sh
# Commands
create    Create a dataset with custom parameters.
fromjson  Create a dataset from json file.

# Show all options for command
python dataset_cli/app.py create --help
python dataset_cli/app.py fromjson --help

# Create dateset from cli
python dataset_cli/app.py create --start 2018-09-24 --target fact --source slack --random 123

# Create dateset from json
python dataset_cli/app.py fromjson --file xxxxxx.json

# Custom mongo uri
python dataset_cli/app.py fromjson --mongo mongodb://remote_host:27017 --file xxxxxxx.json

# Folder to export dataset
python dataset_cli/app.py create --target fact --source editor --random 123 --folder my_folder
```

## Release

In order to generate a new release you will need:

**Node LTS**

```sh
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.35.3/install.sh | bash
nvm install lts # You may need to open a new shell
```

**[standard-version](https://www.npmjs.com/package/standard-version)**

```sh
npm install --global standard-version
```

Just run the following script. It will:

- Checkout to master
- Merge the most recent develop changes
- Update package version and create a new tag
- Push changes to master and develop

```sh
./scripts/release
```

## Test

Run every tests

```sh
poetry run pytest
```

## Coverage

Run tests and generate a code coverage report

```sh
poetry run coverage run -m pytest
poetry run coverage report -m

# Remove a previously generated coverage reports
poetry run coverage erase
```
ezai-env 0.0.1
Environment for EZAI
Requires Python: >=3.6

# ezai_env
## Description
Docker container and conda virtual environment creation for doing AI on local machines, Kubernetes or SageMaker.

## Pre-requisites for building
- Linux: Python is Installed
- Windows: Python is installed, preferably miniconda.

## Usage

### EZAI in docker environment with conda:

Note: This version maps `$home` directory and `/mnt` directory on the host to docker container, so be careful with modifying anything in these folders inside the container, it will also be modified on the host

Do the following first:
- Download the two files: 
`curl -O "https://raw.githubusercontent.com/armando-fandango/ezai_env/main/{.env,ezai-docker}"`
- modify `.env` file as needed, specially the `itag`
- execute `source ezai-docker`
- now use any of the following commands

#### To run EZAI container
- `ezai_runc`
    - pulls the image if not present
    - starts the container if not started
    - enters the container
- I generally create an alias in bash to run the notebooks from the container quickly:
`alias eznb='conda activate ezai && jupyter notebook --ip=* --no-browser'`

#### To pull EZAI image:
- `ezai_pull_image`

#### To remove EZAI container
- `ezai_rmc`

#### To remove EZAI image
- `ezai_rmi`

#### To build EZAI image
- `ezai_build_image`

### EZAI conda environment on your local machine (no docker):

- modify `ezai-conda-req.txt` as needed
- modify `ezai-pip-req.txt` as needed
- for linux: execute `ezai-conda-create.sh --venv <location-of-env>  --python-ver <python-version>`
    - for windows: execute `ezai-conda-create.ps1 -venv <location-of-env>  -python-ver <python-version>`
    - `<location-of-env>`: Default is `/opt/conda/envs/ezai` on linux and `C:/Miniconda3/envs/ezai` on windows
    - `<python-version>`: Defalult is `3.7`
    - You can supply your own `requirements.txt` files with `--piptxt` and `--condatxt`.
- activate the environment with `conda activate <location-of-env>`
- test the tensorflow and pytroch GPU with `pytest -p no:warnings -vv`

### EZAI conda environment in AWS SageMaker instance:

The SageMaker instance should be already created and stopped before following these steps.

#### On your laptop
- Configure `awscli` if not already done: `aws configure`
- Clone the repo : `git clone https://github.com/armando-fandango/ezai_env.git`
- Go to the `ezai_env` folder where you cloned the repo
- source the file `source ezai-conda` 
- Set the SageMaker env: `set_sagemaker_env -i <instance_name>`

#### Prepare the SageMaker instance
- Launch the instance and open a terminal
- In the terminal: `cd /home/ec2-user/SageMaker/ezai_env`
- Create conda environment: `./ezai-conda-create.sh --venv /home/ec2-user/SageMaker/envs/ezai`
- activate the environment with `conda activate /home/ec2-user/SageMaker/envs/ezai`
- test the tensorflow and pytorch GPU with `pytest -p no:warnings -vv`

#### Test it
- Open the `test.ipynb` file

## TODO:

- Give option to create fully isolated container
- Give option to select which libraries to install
- add/enable more libraries
- make separate dockerfile for Sumo and create only if asked from the run
aws-sec-graph 0.0.1
A AWS Security graph generator
Requires Python: >=3.6

# aws-sec-graph

# Purpose

Give a graph of the AWS Security Groups and EC2 instances relation.

This allow you to see relations over *instances* **X** *security groups* **X** *opened ports* based on AWS Filters(with boto3, Port filters or in all ec2 available instances and Security Groups.

# Requirements

### Packages

- python3
- awscli
- graphviz
- pip
- boto3 


### Prerequisites

- awscli configured
- sudo apt install graphviz (https://graphviz.org/download/)
- install graphviz (https://graphviz.readthedocs.io/en/stable/manual.html#installation)

### Installation

    pip install requirements.txt

### AWS Permissions
*Read-Only*
    
*Permissions:*

    ec2:DescribeInstances
    ec2:DescribeTags
    ec2:DescribeSecurityGroups

*Resources:*

    *(All)

*Example:*
 
    {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Sid": "VisualEditor0",
                "Effect": "Allow",
                "Action": [
                    "ec2:DescribeInstances",
                    "ec2:DescribeTags",
                    "ec2:DescribeSecurityGroups"
                ],
                "Resource": "*"
            }
        ]
    }

# Options:
    
    --command : 
        Only "analyze" command available
    --profile  : Inform aws profile to authenticate using boto3 lib
    --filter : Filter to apply on describe instances (https://docs.aws.amazon.com/cli/latest/reference/ec2/describe-instances.html)
    --format : output format (https://graphviz.org/doc/info/output.html)
    --port-filter : Inform a especific port or a range of ports to filter the output, example: 1 unique port "22" or range of ports "20:22"

# Usage

### General usage
   
    python3 run.py --command=analyze

### Using filter

     python run.py --command=analyze --profile=default --filter='[{"Name": "tag:environment","Values": ["staging"]}]'

### Using port filter
       
Range of ports:
     
     python run.py --command=analyze --profile=default --port-filter=20:22
     
Unique port:
     
     python run.py --command=analyze --profile=default --port-filter=22
     
 ## Change output format (based on graphviz lib)

    python run.py --command=analyze --profile=default --filter='[{"Name": "tag:environment","Values": ["staging"]}]' --format=xdot

# Recomendations

- https://boto3.amazonaws.com/v1/documentation/api/latest/index.html
- https://graphviz.org/documentation/
- https://github.com/jrfonseca/xdot.py

# TODO 
1. Use aws profile: done (08/11/2020)
2. Filter for instances to graph: done (08/11/2020)
3. Filter for ports: done (18/12/2020)
4. Generate different page for security group
ayx-plugin-sdk 0.1b4
The Python plugin SDK for Alteryx Designer.
Requires Python: >=3.8

# Alteryx Plugin SDK (ayx-plugin-sdk) for Python

## Overview
The Alteryx Plugin SDK can be used to extend the functionality of Alteryx Designer
by allowing developers to create custom tools/plugins.

This package is currently in an alpha state of development, and the interfaces are
subject to change in subsequent releases.

## Support
Copyright 2020 Alteryx Inc.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated 
documentation files (the "Software"), to deal in the Software without restriction, including without limitation the 
rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit 
persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the 
Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE 
WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR 
OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
bit38 1.0.0
Secure your Bitcoin private keys with a passphrase

# Bip38

> A Python package to secure your Bitcoin private keys with Bip38.

```
from bit38 import encrypt, decrypt

encrypt(private_key, passphrase) #=> 6PRVWUbkzzsbcVac2qwfssoUJAN1Xhrg6bNk8J7Nzm5H7kxEbn2Nh2ZoGg
decrypt(private_key, passphrase) #=> 5KN7MzqK5wt2TP1fQCYyHBtDrXdJuXbUzm4A9rKAteGu3Qi5CVR

```

Credits: https://github.com/darosior/bitcoineasy

References: https://github.com/bitcoin/bips/blob/master/bip-0038.mediawiki