semvec/pypi-packages huggingface dataset:
tfsx 1.2.14
A Wrapper to access information from Microsoft DevOps

# Goal
This lib is reponsable for retriving data and sending commands to Microsoft DevOps.

## Installation
To install tfsx, run this command in your terminal:

```
pip install tfsx
```
## Usage
To use tfsx in a Python project:

```python
from tfs.tfs import TFS
from pprint import pprint 
from datetime import datetime
def main():

    tfs = TFS('<userkey>','<serverurl')

    work_itens = tfs.get_work_item_query_by_wiql_epic_user_story_product_backlog_item()
    pprint (len(work_itens))

    work_itens = tfs.get_work_item_query_by_wiql_task()
    pprint (len(work_itens))    

if __name__ == "__main__":
    main()
```
alanbootstrap 0.2.0
Boostrap Module for Alan

# Alan Bootstrap

## Active Learning 

#### Setup Environment
1. Create a Conda Environment for Python 3.7.3:
```bash
conda create --name <EVN_NAME> python=3.7.3
```
2. Git Clone:
```bash
git clone https://<USR_NAME>@bitbucket.org/rbcmllab/data_bootstrap.git
```
3. Install Python Dependencies
```bash
cd data_bootstrap/alanbootstrap
pip install -r requirements.txt
pip install alanbootstrap
```

#### Run
```bash
> python bin/ast_al_bin.py -h

usage: ast_al_bin.py [-h] -i INIT -p POOL -o OUTPUT [-d DEVSET]

Parameters for AST classification active learner. The model will be persisted
into the output folder at every iteration. If a dev dataset was provided, the
classification accuracy score would be calculated on this dataset at each
iteration and a performance plot would be saved into the output folder.

optional arguments:
  -h, --help            show this help message and exit
  -i INIT, --init INIT  Shared-AST json file path; ASTs in this file will be
                        used to initialize the active learner. Every AST in
                        this file must contain a target value and a list of
                        complexity_features.
  -p POOL, --pool POOL  Shared-AST json file path; Active learner will sample
                        ASTs from this file Every AST in this file must
                        contain a list of complexity_features.
  -o OUTPUT, --output OUTPUT
                        Absolute path of the model persisting folder.
  -d DEVSET, --devset DEVSET
                        Shared-AST json file path; ASTs in this file will be
                        used to evaluate the active learner. Every AST in this
                        file must contain a target value and a list of
                        complexity_features.
```
Example:
```bash
> python bin/ast_al_bin.py \
-i $DATA_DIR/ast_train.json \
-p $DATA_DIR/ast_pool.json \
-o $DATA_DIR \
-d $DATA_DIR/ast_dev.json
```
ad1459 1.19.0
An IRC Client
Keywords: ad1459 irc client chat gui gtk

# ad1459
IRC Client

AD1459 is an IRC client written in Python and GTK3. It aims to be a modern IRC 
client with features that make sense for IRC today. It has an interface which 
has been loosely inspired by Hexchat and mIRC.

![AD1459 IRC Client](https://i.imgur.com/JuIuJY6.png)

AD1459 is currently in _ALPHA_, and should not be considered ready for everyday 
use. That being said, it is a relatively capable client for basic functionality 
even in its currently incomplete state.

## Current Abilities

* Multiple-network support
* Chatting over IRC.
* Joining/Parting channels
* Changing nick
* Tab-completion
* Save and recall servers
* Secure password storage within system keyring
* Notifications
* User list
* Topic
* Some commands for doing IRC Things
* Last message recall
* Compacted server messages


## TODOS

Currently planned features include:

* CTCP
* Logging


## Known Issues

These are problems that have been currently identified:

* Large buffers make the application unresponsive
* CTCP ACTION messages sent from the client also highlight the client.

### Connecting to IRC

To connect to a server/network, click on the server button (in the top left) and
enter the server details in the text entries. You can alternatively enter a 
server as a single line of text, for which the format is:

`none|sasl|pass name host port username (tls) (password)`

#### `none|sasl|pass`

This specifies the connection type. If you need to authenticate to the server 
with a server password, then use `pass`. If the network supports using SASL, use
`SASL`.

#### `name`

This is the name for the network in the list. (e.g. `freenode`, `Esper`)

#### `host`

The hostname of the server to connect to, e.g. `chat.freenode.net`

#### `port`

The port to connect with, e.g. `7070`. Default is 6697.

#### `username` 

The username/ident for your connection to the server. This will also be your 
initial nickname (Separate nickname support is planned for a future release)

#### `tls`

If present, AD1459 will use TLS to connect to the server. Otherwise, a plaintext
connection will be used.

#### `password`

The password to use to authenticate with the server. This option is required if
the authentication method specified was `sasl` or `pass`. It should be omitted
otherwise.

### Example connection lines

`sasl Esper irc.esper.net 6697 jeans tls hunter2`

`none freenode chat.freenode.net 6666 g4vr0che`

`pass My-Private-Network my.private-network.com 12345 secret_username tls hunter3`
ubibotpy 0.0.1
Python implementation for Ubibot APIs.
Requires Python: >=3.6

# ubibotpy
Ubibot API wrapped in Python implementation library.

Currently, only supports application key access to all "channels" (devices) associated to the account.

The base class is a Channel. Each channel has accessor methods to return the latest data from the channel.

Channels object holds multiple channel. All channels are held in a dictionary keyed on their channel_id.