semvec/pypi-packages huggingface dataset:
vault-dump-restore 0.5.1
Dumps keys from a Hashicorp Vault instance to be able to restore it on another instance
Requires Python: >=3.6, <4
Keywords: hashicorp,vault,keys,secrets,backup,restore

# vault-dump-restore

Dumps keys from a Hashicorp instance into a file, to be able to restore it later
in any other instance. Remember to encrypt the file!

This only supports reading a kv2 engine. Also note that these dumps and
restores are meant to be used very rarely and under emergency situations
since this kind of defeats the purpose of having a vault (storing secrets
inside a vault, sealed with a key, etc) - you're dumping all the secrets
to a external file!

The following examples use gpg commands to encrypt/decrypt the files.

Example for dump:

```bash
# make sure you have the vault environment variables:
export VAULT_ADDR=https://<vault-addr>
export VAULT_TOKEN=<vault-token>

# dumps the entire vault kv secrets in a shell script (vault commands)
vault-dump -o vault vault -o | gpg --symmetric --cipher-algo AES256 > vault-backup.sh.enc

# dumps a specific path with the default json format
vault-dump -p secrets/specific-app | gpg --symmetric --cipher-algo AES256 > vault-backup.json.enc
```

Examples for restore:

```bash
# make sure you have the vault environment variables:
export VAULT_ADDR=https://<vault-addr>
export VAULT_TOKEN=<vault-token>

# you will also must have the secret engine created
vault secrets enable -path=secrets kv

# restore a full dump using the shell script format
. <(gpg -qd vault-backup.sh.enc)

# restore a full dump using the JSON format
gpg -qd vault-backup.json.enc | vault-restore -

# restore a specific path using the JSON format
gpg -qd vault-backup.json.enc | vault-restore -p secrets/specific-app -
```

## Installation and usage

Installation can be done through pip:

```bash
pip install vault-dump-restore
```

Use the `-h` parameter to get help from the commands:

```bash
vault-dump -h
vault-restore -h
```

## Contents

* [API Reference](api_ref.md)
* [ChangeLog](changelog.md)

## Setup and usage for local development

Make a virtual environment:

```bash
python3 -m venv venv
source venv/bin/activate
pip install -e .
```

Note that this will also install the local dependencies, which might change after
some time. If needed, you can run `pip install -e .` again to reinstall the
updated dependencies anytime.

## Documentation build

Run:

```bash
make docs
```

HTML documentation will be generated at `docs/_build/html`.
projeto-calculadora 0.0.1
O projeto calculadora, meu primeiro pacote
Requires Python: >=3.5

# projeto_calculadora

DescriÃ§Ã£o.
O pacote projeto_calculadora tem uma estrutura:
    Processos:
        - Entrada dos dados
        - SaÃ­da dos dados

## InstalaÃ§Ã£o

Use o gerenciador de pacote [pip](https://pip.pypa.io/en/stable/) para instalar o projeto calculadora

'''bash
pip install projeto_calculadora
'''
## Author
Daniel Pereira
pytest-wiremock 0.1.0
A pytest plugin for programmatically using wiremock in integration tests
Requires Python: >=3.8,<4.0

# pytest-wiremock
A pytest plugin and DSL for easy integration testing with wiremock :rocket:

[![PyPI version fury.io](https://badge.fury.io/py/pytest-wiremock.svg)](https://pypi.python.org/pypi/pytest-wiremock/)
[![codecov](https://codecov.io/gh/symonk/pytest-wiremock/branch/main/graph/badge.svg?token=DT2823RGAG)](https://codecov.io/gh/symonk/pytest-wiremock)

# Usage:

```python
# Todo: Update.
```


# Contributing
---------------

 - Install `docker`.
 - Install `docker-compose`.
 - Install `poetry`.
 - Install `tox`.
 - Run `pre-commit install`.
 - Create and branch and make changes.
 - execute `tox -e py38`
 - Hooks will automatically run; NO PR's will be accepted with a decrease in coverage.
oak-cravat 2.3.13
OakCRAVAT, further development of OpenCRAVAT by its lead architect
Requires Python: >=3.6

OakCRAVAT is a genomic variant analysis platform, based on `OpenCRAVAT`_ and further developed by Ryangguk Kim, the lead architect of OpenCRAVAT.

.. _OpenCRAVAT: https://github.com/KarchinLab/open-cravat

OakCRAVAT uses the same commands as OpenCRAVAT. Thus, to use OakCRAVAT do `pip uninstall open-cravat` and `pip install oak-cravat`. The same `oc` commands will work.

For documentation, see `OpenCRAVAT Documentation`_

.. _OpenCRAVAT Documentation: https://open-cravat.readthedocs.org

Below is the readme of OpenCRAVAT:

 Open-CRAVAT is a python package that performs genomic variant interpretation including variant impact, annotation,                         
 and scoring.  It has a modular architecture with a wide variety of analysis modules that are developed both by
 the CRAVAT team and the broader variant analysis community. Open-CRAVAT is a product of the `Karchin Lab`_
 at Johns Hopkins University in collaboration with `In Silico Solutions`_ with funding provided by the National
 Cancer Institute's `ITCR`_ program.

 .. _Karchin Lab: http://karchinlab.org
 .. _In Silico Solutions: http://insilico.us.com
 .. _ITCR: https://itcr.cancer.gov

 Please see: `Open-CRAVAT Docs`_ for installation instructions and software documentation.

 .. _Open-CRAVAT Docs: https://github.com/KarchinLab/open-cravat/wiki
aio-exos 0.4.0
Extreme EXOS asyncio client for JSON-RPC and RESTCONF

**WORK IN PROGESS**

# Extreme EXOS asyncio Client

This repository contains an Extreme EXOS asyncio client, support for both
JSON-RPC and RESTCONF options.

For reference on the EXOS JSON-RPC, refer to [this
document](https://documentation.extremenetworks.com/app_notes/MMI/121152_MMI_Application_Release_Notes.pdf).

For reference on the EXOS RESTCONF support, refer to [this
document](https://api.extremenetworks.com/EXOS/ProgramInterfaces/RESTCONF/RESTCONF.html
).

### Device Configuration

In order to access the EXOS device via API you must enable the web server
feature using either http or https.

```text
enable web http
enable web https    # requires ssl configuration as well.
```

### JSON-RPC Usage

```python
from aioexos.jsonrpc import Device

dev = Device(host='myhostname', username='user', password='Random')
show_one = await dev.cli('show switch')
show_many = await dev.cli(['show switch', 'show version'])

# get text instead of JSON/dict

show_text = await dev.cli('show switch', text=True)
```

### RESTCONF Usage

The RESTCONF API supports only JSON body at this time.  XML is not supported
even though the documentation states that it does.

```python
from aioexos.restconf import Device

dev = Device(host='myhostname', username='user', password='Random')

# login step required for session authentication

await dev.login()

# execute commands providing the restconf URL, supports all request methods
# (GET, POST, etc.)

res = await dev.get('/openconfig-system:system')

# close connection when done with commands
await dev.aclose()
```
bioturbation 1.0.1
Model of earthworm bioturbation in a layered soil profile
Requires Python: >=3.6

# Bioturbation Model

[![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8B-yellow)](https://fair-software.eu) [![PyPI version](https://badge.fury.io/py/bioturbation.svg)](https://badge.fury.io/py/bioturbation)

A simple model of earthworm bioturbation, using a layered soil profile. The model predicts the mixing of mass pools (e.g. of contaminants) over time as a function of earthworm density.

## Quickstart

The model can be installed using `pip`:

```bash
$ pip install bioturbation
```

Alternatively, you can download the source code and use the provided Conda environment file to install the relevant dependencies:

```bash
$ git clone https://github.com/samharrison7/bioturbation-model.git
$ cd bioturbation-model
$ conda env create -f environment.yml
```

## Usage

Check out the [examples](./examples/) directory for examples of how to use the model.

## Conceptualisation

See the [conceptualisation](./examples/conceptualisation.ipynb) notebook for a mathematical description of how the model works.

## Limitations

No guarantee is given as the to scientific validity of this model for your data. The onus is on the user to verify this validity. Indeed, there are a number of limitations to the model as it stands which might limit its applicability:
* Transport is only between *adjacent* soil layers, therefore neglecting preferential flow pathways, e.g. representing deep burrowing earthworms transporting tracers directly from upper to lower layers.
* No change in soil properties due to bioturbation is modelled. That is, there is an implicit assumption that the amount of soil mixed between layers is negligible. This assumption might not always be valid.

We welcome requests for collaboration if you are interested in exploring the validity of the model for your own research. Please [get in touch](mailto:sharrison@ceh.ac.uk).