semvec/pypi-packages huggingface dataset:
vonixsync 1.0.12
Database syncronizer
Requires Python: >=3.9
Keywords: syncronizer,vonix

# vonixsync

vonixsync is a python library for syncronization/database mirroring of the vonix database

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install vonixsync.

```bash
pip install vonixsync
```

## Usage

```python
import vonixsync
from vonixsync import DBConfigs
from vonixsync import Syncronizer

#Import the DBConfigs class. Provide the parameters used to construct the string used to connect to the database, according to its singular dialect.
connection_configs = DBConfigs(
    database_manager="postgres",
    user="postgres",
    password="postgres",
    hostname="localhost",
    database="postgres",
    port=5432,
)

connection = connection_configs.connect

# Declare your token as a string type
token = "token_provided_by_vonix_support"

#Import the Syncronizer Class to effectively syncronize the data to your database and name all tables 
Syncronizer(
    
    token=token,
    
    connection=connection,

    agents="agent",
    agent_event = "agent_event",
    agent_pause="agent_pause",
    agent_summary="agent_summary",
    
    calls="call",
    call_ratings="call_rating",
    
    chats="chat",
    chat_message="chat_message",
    
    profilers="profiler",
    trees="trees",
    
    queues="queue",
    queue_summary= "queue_summary",
    
    fromPeriod=1678449585,
    
    echo=True

).syncronize()

```
Now run the code.
#
## Syncronizer Options

Besides the obligatory token, database_string parameters and names of the tables to be syncronized, the Syncronizer has other options:
#
### fromPeriod
#
The timestamp parameter must be declared in timestamp format. It is an obligatory filter for the summary tables.

- the syncronizer will look for the most recent inserted row in the mirrored database and mirror from this row's date on. 

- If no data is found in the mirrored database the syncronizer will mirror data using the fromPeriod timestamp value provided to the Syncronizer.

- If no timestamp parameter was provided, the Syncronizer will use the timestamp from the day before the current date.

```python
Syncronizer(token, database, agents= "agent", fromPeriod = 1679067723 ).syncronize()
```
#
### echo
#
The echo parameter by default is False. But if declared as True, it will enable the logging of all SQL commands during the active phase of the syncronizer.
This a feature provided by the SQLAlchemy library. It can be set with or without other optional parameters.

```python
Syncronizer(token, database, queues = "queue", echo = True ).syncronize()
```
Gaiatri 0.0.9
Ultra high-level Python library for processing and analysing geo-data.
Keywords: python,geospatial,geographical information systems,telemetry

This library further abstracts Python libraries such as Pandas, Matplotlib, and Numpy so that non-developers can use Python to process and analyse GNSS data, telemetry data, and other spatial-temporal data. May it enlighten users with understanding, like the goddess Gayatri - on mother earth (Gaia) and beyond..
ita-converter 0.9.3
ita-converter - convert your images to ASCII art easily!

# ita-converter

ita-converter is a lightweight and fast Python library that converts an image to ASCII art. It allows users to resize images, convert them to grayscale, and customize the symbols used to create the ASCII art. 

## Installation

You can install ita-converter using pip:
```
pip install ita-converter 
```

## Usage

To use ita-converter, simply import the `get_ascii_art()` function and pass in the path to the image file you want to convert. By default, the function will use a set of ASCII characters to create the art, but you can also pass in your own symbol set.

```python
from ita_converter import get_ascii_art

ascii_art = get_ascii_art("path/to/image.png", symbol_set=["@", "#", "*", " "])
print(ascii_art)
```
By default, the function will use color in the output. If you want a black and white output, set `color` to `False`.
```python
ascii_art = get_ascii_art("path/to/image.png", color=False)
print(ascii_art)
```
You can also resize the image and specify the width and height of the output. If you only specify the width, the height will be adjusted automatically to maintain the aspect ratio.
```python
ascii_art = get_ascii_art("path/to/image.png", width=50, height=50)
print(ascii_art)
```
You can specify the `colour_type`. By default it is ANSI, but you can also set it to HTML.
```python
ascii_art = get_ascii_art("path/to/image.png", colour_type="html")
print(ascii_art)
```
or
```python
ascii_art = get_ascii_art("path/to/image.png", colour_type="ansi")
```
## Contributing

Contributions are welcome! If you have an idea for a new feature or want to report a bug, please open an issue. If you want to contribute code, please fork the [repository](https://github.com/LyubomirT/ita-converter), make your changes, and submit a pull request.

## License

ita-converter is licensed under the [MIT License](https://opensource.org/licenses/MIT).
scipionn-toolkit 1.0.4
Xmipp neural network utilities for flexibility
Keywords: scipion continuous-heterogeneity imageprocessing xmipp

=======================
Scipionn Toolkit
=======================

This package provides a neural network environment integrating the deep learning algorithms needed by `scipion-em-flexutils <https://github.com/scipion-em/scipion-em-flexutils>`_ plugin

==========================
Included Networks
==========================

- **Zernike3Deep**: Semi-classical neural network to analyze continuous heterogeneity with the Zernike3D basis
- **DeepNMA**: Semi-classical neural network with automatic NMA selection directly from images to analyze continuous heterogeneity
- **HomoSIREN**: Neural network homogeneous reconstruction for real space
- **HetSIREN**: Neural network heterogeneous reconstruction for real space
- **DeepPose**: Particle pose and shift refinement with neural networks
- **FlexConsensus**: Consensus neural network for conformational landscapes

==========================
Installation
==========================

The Flexutils-Tensorflow toolkit relies on Conda and Pip for its correct installation.

Before installing, Conda must be set in the PATH variable so it can be discovered during the installation. Once this requirement is met, the package can be either installed with ``pip install scipionn-toolkit`` or with ``pip install -e path/to/cloned/scipionn-toolkit`` after cloning this repository.

We recommend adding the flag `-v` to pip installation command to have a better tracking of the installation.