semvec/pypi-packages huggingface dataset:
proton-api-client 0.0.8
Proton Mail API
Requires Python: >=3.10.10
Keywords: proton api client async search automation bot scrape mail email



### Proton Mail API

#### Examples

```python
from proton.client import ProtonMail

username, password = ..., ...
proton = ProtonMail(username, password)

passphrase = 'myPass'
pk = 'privatekey.test123@proton.me-6fdhskjgfd7s98gdgre87gregrjdhrgd7897g898.asc'
proton.gpg_import(pk, passphrase=passphrase)

sessions = proton.sessions()

salts = proton.salts()

users = proton.users()

inbox = proton.inbox()

cid = inbox['Conversations'][0]['ID']
conversation = proton.decrypt_conversation(cid)

decrypted_inbox = proton.inbox_decrypted()

user_settings = proton.user_settings()

mail_settings = proton.mail_settings()

calendar_settings = proton.calendar_settings()

timezones = proton.timezones()

addresses = proton.addresses()

info = proton.info()

directory = proton.calendar_directory()

version = proton.version()

plans = proton.plans()

calendar_directory = proton.calendar_directory()

proton.revoke_all_sessions()

```
rockset-sqlalchemy 0.0.1
Rockset's SQLAlchemy support and DB-API specification
Keywords: Rockset,rockset-client

# rockset-sqlalchemy
This library implements Python's dbapi spec and provides SQLAlchemy support on top of Rockset.

## Usage
To install the latest version of this package, run:

    pip3 install rockset-sqlalchemy

To connect to the database:

```python
from sqlalchemy import create_engine

engine = create_engine(
    "rockset://",
    connect_args={
        "api_key": "{your api key}",
        "api_server": "{your api server}"
        "virtual_instance": "{your virtual instance ID}" # virtual_instance is optional
    },
)
```

See some example queries [here](https://github.com/rockset/rockset-sqlalchemy/blob/main/example.py). See the SQLAlchemy Unified Tutorial [here](https://docs.sqlalchemy.org/en/20/tutorial/index.html).

## Development
Iterating on this library is very simple.

First, clone the source repository:

    git clone https://github.com/rockset/rockset-sqlalchemy

Then, all you need to do is run `sudo python3 setup.py develop` from the cloned directory and hack away.

You can use the example script `example.py` to get started with development. Make sure you provide a `ROCKSET_API_KEY` and `ROCKSET_API_SERVER` to the script, like so

```
ROCKSET_API_KEY=xxx ROCKSET_API_SERVER=https://api.rs2.usw2.rockset.com python3 example.py
```
heyvi 0.2.28
Project Vi
Keywords: computer vision machine learning ML CV privacy video image

[![PyPI version](https://badge.fury.io/py/heyvi.svg)](https://badge.fury.io/py/heyvi)

\"Hey Vi!\"
-------------------

HEYVI: Analytics for Visual AI    
docs: https://developer.visym.com/heyvi

HEYVI is a python package for visual AI that provides systems and trained models for activity detection and object tracking in videos.

HEYVI provides:  

* Real time activity detection of the [CAP activity classes](https://visym.github.io/cap)
* Real time activity detection of the [MEVA activity classes](https://mevadata.org)
* Real time visual object tracking in long duration videos
* Live streaming of annotated videos to youtube live
* Visual AI from RTSP cameras


Requirements
-------------------
python 3.*    
[ffmpeg](https://ffmpeg.org/download.html) (required for videos)    
[vipy](https://github.com/visym/vipy), torch, pytorch_lightning (for training)    


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

```python
pip install heyvi
```


Quickstart
-------------------
```python
v = heyvi.sensor.rtsp().framerate(5)
T = heyvi.system.Tracker()
with heyvi.system.YoutubeLive(fps=5, encoder='480p') as s:
     T(v, frame_callback=lambda im: s(im.pixelize().annotate()))
```

Create a default RTSP camera and GPU enabled object tracker, then stream the privacy preserving annotated video (e.g. pixelated bounding boxes with captions) to a YouTube live stream.


[![YoutubeLive stream](https://img.youtube.com/vi/rMuuRpBCaVU/maxresdefault.jpg)](https://youtu.be/rMuuRpBCaVU)