semvec/pypi-packages huggingface dataset:
fireeyepy 1.3.0
FireEye Client Library for Python
License: MIT
Requires Python: >=3.6

[![PyPI version](https://badge.fury.io/py/fireeyepy.svg)](https://badge.fury.io/py/fireeyepy)
[![Python versions supported](https://img.shields.io/pypi/pyversions/fireeyepy.svg)](https://pypi.python.org/pypi/fireeyepy)

# FireEye Client Library for Python
This is the Python client library for all things FireEye API. Currently it only supports FireEye's Detection On Demand but will have support for other FireEye API's soon.

For more API information, visit the [FireEye Developer Hub](https://fireeye.dev)

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

To install the Python client library:
```
pip install fireeyepy
```

To upgrade your installed library:
```
pip install fireeyepy --upgrade
```

Alternatively, you can clone the repository via the command line:
```
git clone https://github.com/fireeye/fireeye-python.git
```

Usage
-----
Begin by importing the 'fireeye' module:
```python
import fireeyepy
```

## Detection On Demand
Construct a Detection object with your api key:
```python
detection = fireeyepy.Detection(key=api_key)
```
To obtain a free trial API key, subscribe on the [AWS Marketplace](https://aws.amazon.com/marketplace/pp/B07XSMKK41)

### Upload A File
```python
  import fireeyepy

  detection = fireeyepy.Detection(key="yourapikeyhere")

  result = detection.submit_file(
    files={
      "file": ('filename', open('./path/to/filename', 'rb'))
    }
  )
```
With configuration options:
```python
  result = detection.submit_file(
    body={
      "file_name": "different_name.txt",
      "screenshot": true
    },
    files={
      "file": ('filename', open('./path/to/filename', 'rb'))
    }
  )
```

### Submit URLs
```python
  import fireeyepy

  detection = fireeyepy.Detection(key="yourapikeyhere")

  result = detection.submit_urls(["url1","url2",...])
```

### Retrieve File or URL Report
```python
response = detection.get_report(report_id)
```
You may also provide the optional `extended=True` flag to get the full, in-depth report:
```python
response = detection.get_report(report_id, extended=True)
```

### Search for a file from a connector (ex. Box, Teams, S3, etc.)
```python
response = detection.search_reports(connector_type="box", file_id="123456")
```

### Get a list of reports
**Start and end time are in Unix epoch time.**
```python
response = detection.telemetry(start_time=1603980060, end_time=1604066460)
```

### Retrieve Presigned URL for Dashboard Report
```python
result = detection.get_presigned_url(report_id)
```

### Perform Hash Lookup
```python
response = detection.get_hash(hash)
```

### Get a report artifact
```python
artifact = detection.get_artifact(report_id="8d0aa90b-8bf3-4483-ae3b-0ded00d157ab", artifact_type="screenshot")
```

### Get the health of the Detection on Demand service
```python
health = detection.get_health()
```
rfc3161ng 2.1.3
Python implementation of the RFC3161 specification, using pyasn1
License: MIT

=========
rfc3161ng
=========

.. image:: https://img.shields.io/pypi/l/rfc3161ng.svg
   :target: https://raw.githubusercontent.com/trbs/rfc3161ng/master/LICENSE

.. image:: https://github.com/trbs/rfc3161ng/workflows/CI/badge.svg?branch=master
     :target: https://github.com/trbs/rfc3161ng/actions?workflow=CI
     :alt: CI Status

.. image:: https://img.shields.io/pypi/v/rfc3161ng.svg
    :target: https://pypi.python.org/pypi/rfc3161ng/
    :alt: Latest PyPI version

.. image:: https://img.shields.io/pypi/wheel/rfc3161ng.svg
    :target: https://pypi.python.org/pypi/rfc3161ng/
    :alt: Supports Wheel format

A simple client library for cryptographic timestamping service implementing the
protocol from RFC3161.

This started as a fork of https://dev.entrouvert.org/projects/python-rfc3161 and
has some additional patches such as Python3 support.

The latest version of this library is available from
https://github.com/trbs/rfc3161ng/ .


Public providers
================

There are several timestamping services around.  Here is a list of
publicly available services you can try:

 * http://freetsa.org/tsr
 * http://time.certum.pl
 * http://timestamp.comodoca.com/rfc3161
 * http://timestamp.geotrust.com/tsa
 * http://timestamp.globalsign.com/scripts/timstamp.dll
 * http://tsa.starfieldtech.com
 * https://teszt.e-szigno.hu:440/tsa

Example
=======

    >>> import rfc3161ng
    >>> certificate = open('data/certum_certificate.crt', 'rb').read()
    >>> rt = rfc3161ng.RemoteTimestamper('http://time.certum.pl', certificate=certificate)
    >>> tst = rt.timestamp(data=b'John Doe')
    >>> rt.check(tst, data=b'John Doe')
    True
    >>> rfc3161ng.get_timestamp(tst)
    datetime.datetime(2017, 8, 31, 15, 42, 58, tzinfo=tzutc())

Example for a server that insist on SHA256:

    >> import rfc3161ng
    >> timestamper = rfc3161ng.RemoteTimestamper('https://interop.redwax.eu/test/timestamp', hashname='sha256')
    >> tsr = timestamper(data=b'The RedWax Project', return_tsr=True)
    >> print('{}'.format(tsr))

Verifying timestamp using OpenSSL
=================================

One can verify the timestamp returned by the timeserver by using OpenSSL.
For example with:

  $ openssl ts -verify -data data_file.txt -in data_file.tsr -CAfile cacert.pem -untrusted tsa.crt

To save the tsr you can use code similar to:

    >>> from pyasn1.codec.der import encoder
    >>> import rfc3161ng
    >>> ...
    >>> timestamper = rfc3161ng.RemoteTimestamper('http://freetsa.org/tsr', certificate=certificate_data)
    >>> tsr = timestamper(data=data_file.read(), return_tsr=True)
    >>> with open("data_file.tsr", "wb") as f:
    >>>     f.write(encoder.encode(tsr))

Alternatively you can just save the raw `response.content` returned from the certification server.

There is a test which also covers this in `test_verify_timestamp_response_with_openssl`.


Authors
=======

 * Benjamin Dauvergne <bdauvergne@entrouvert.com>
 * Michael Gebetsroither <michael@mgeb.org>
 * Bas van Oostveen <trbs@trbs.net>
video-razor 0.0.3
A package for slicing a video into N sections
License: MIT
Requires Python: >=3.6
Keywords: OPENCV,VIDEO,CUT,SLICE,RAZOR

[![Build Status](https://travis-ci.com/5starkarma/face-smoothing.svg?branch=main)](https://travis-ci.com/5starkarma/face-smoothing) [![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)

# Video Razor

1 | 2 | 3
:-------------------------:|:-------------------------:|:-------------------------:
![alt text](https://github.com/5starkarma/video-razor/blob/master/data/examples/section-1.png?raw=true "Input image")  |  ![alt text](https://github.com/5starkarma/video-razor/blob/master/data/examples/section-2.png?raw=true "Output image") |  ![alt text](https://github.com/5starkarma/video-razor/blob/master/data/examples/section-3.png?raw=true "Output image")
4 | 5 | 6
![alt text](https://github.com/5starkarma/video-razor/blob/master/data/examples/section-4.png?raw=true "Input image")  |  ![alt text](https://github.com/5starkarma/video-razor/blob/master/data/examples/section-5.png?raw=true "Output image") |  ![alt text](https://github.com/5starkarma/video-razor/blob/master/data/examples/section-6.png?raw=true "Output image")
7 | 8 | 9
![alt text](https://github.com/5starkarma/video-razor/blob/master/data/examples/section-7.png?raw=true "Input image")  |  ![alt text](https://github.com/5starkarma/video-razor/blob/master/data/examples/section-8.png?raw=true "Output image") |  ![alt text](https://github.com/5starkarma/video-razor/blob/master/data/examples/section-9.png?raw=true "Output image")
---

A package for slicing a video into N**2 sections. 

The original use-case was to process higher-definition videos, split up, through Deep Learning models. This allows for easier detection of smaller objects.

---
## Install
```
pip install video-razor
```

## Usage
The class input file name, output file name, and the cross sections you'd like to split the video in. e.g. `3` will produce an output of 9 videos - 3 horizontal, 3 vertical.

The output file name should **NOT** contain a suffix as this will be appended during processing.
```
input_file = 'data/input/sample.mp4'
output = 'data/output/out'
razor = VideoRazor(input_file, output, 3)
razor.slice()
```
hdfs-lmdc 2.0.7
Esta biblioteca tem como objetivo generalizar funções da integração entre HDFS e Python utilizando HDFS3 ou JavaWrapper
License: MIT

# hdfs-python
Esta biblioteca tem como objetivo generalizar funções da integração entre HDFS e Python utilizando HDFS3


## Como usar localmente

Para utilizar esta biblioteca localmente, é necessário cloná-la para o diretório onde se encontra o arquivo no qual você deseja importar a mesma. Isto fará com que a biblioteca esteja somente disponível para tal diretório. 

## Como usar globalmente

Utilizar com PyPip.