semvec/pypi-packages huggingface dataset:
syncgit 0.4.4
Sync python dicts, strings ad modules to git repository.
Requires Python: >=3.8
Keywords: syncgit

# syncgit

Sync python dicts, strings and modules to files in git repository.

NOTE: syncgit calls git using subprocess, setup git so it does not ask for username or password,
otherwise you will get a timeout exception.

### Installation

```
python -m pip install syncgit
```

### Documentation

https://syncgit.readthedocs.io/en/latest/

### Example 

```python
from typing import List

import time

from syncgit import Repo, SyncConfig

# This callback is called when changes are pushed to the repo
def update_callback(repo: Repo, changes: List[SyncConfig]) -> None:
    print(f"Updated to commit {repo.commit_hash}")

    for change in changes:
        print(f"Updated {change.name}")

# Create repo class and import files from repository
rp = Repo("example_repo", "git@github.com:RainingComputers/syncgit-test.git", "main")
rp.set_config([
    SyncConfig("about_alice", "alice.json", "json"),
    SyncConfig("about_bob", "bob.yml"),
    SyncConfig("text", "text_file.txt", "text"),
    SyncConfig("hello_module", "say_hello.py")
])

# Register call back
rp.set_update_callback(update_callback)

# Start sync
rp.start_sync()

# Imported files will be available as attributes on the repo class
# Changes are reflected immediately on these attributes real time
try:
    while True:
        time.sleep(1)
        print(rp.about_alice)
        print(rp.about_bob)
        print(rp.text)
        rp.hello_module.say_hello("Alice")
except KeyboardInterrupt:
    print("Stopping sync")
    rp.stop_sync()

```
GraphicalTextClustering 0.0.4
Applies Graphical Clustering on Text data
Requires Python: >=3.7

# GraphicalTextClustering

When business grows, it’s dependency with IT systems also grows proportionally and monitoring the health of this IT systems are the key to ensure smooth functioning of business and IT. This IT systems are continuously monitored and managed by the IT service providers with group of resolvers. Any problem in the system will be reported as Trouble ticket to the resolvers and they take required corrective action to maintain good health of IT systems. The system problems are reported to the resolvers in two ways: 

* **System-generated issues**: Components such as business applications, processes, CPU, disk, and network interfaces are monitored to detect anomalies. The monitoring tools capture and report any abnormal behavior of system components in the form of alerts. 

* **User-generated issues**: The users of IT systems report problems through calls, emails, or chats. 

Ticket contains various information about the reported problem and all of them can be grouped into any one of the below 

* **Datetime fields** Like reported time, response time, resolved time, etc 
* **Categorical fields** which provide generic grouping of the issue like impacted component, system-of-origin, cause code, CTI, etc 
* **Text fields** like Issue Description, Notes, Summary, etc 

Compared to categorical fields, Issue Description will contain more information about the problem, and it can be very effective in identifying the issues occurring in the IT system. It provides crucial information to better understand and control the IT operations. In case of system-generated tickets, this description is automatically generated by the monitoring and alerting tools. This description is often structured and is based on how the alerting tools have been configured to report a problem. However, in case of user-generated tickets, this description contains free-form text written by users. This description is unstructured and contains variations, ill-formed sentences, and spelling and grammar mistakes. 

This project is about how effectively we can extract issue details from the system generated tickets because around 60–70% of the IT system tickets are system generated only.

For more information, kindly vist https://medium.com/@nmani.1191/need-of-graphical-clustering-in-information-extraction-on-it-support-tickets-part-1-introduction-9fb2e5c01f18

```Python
#How to use the package for training and prediction
from GraphicalTextClustering import TextClustering
import pandas as pd
import joblib

# Read the input data
input_tck_data = pd.read_csv('sample_data.csv')
input_text = input_tck_data['Issue Description']
gc_model = TextClustering.GraphicalClustering(input_text, 2)
gc_model.generate_graph_clusters(5)

# Save the model as a pickle in a file
joblib.dump(gc_model, 'gc_model_v1.pkl')

# Load the model from the file
loaded_gc_model = joblib.load('gc_model_v1.pkl')

# Use the loaded model to make predictions
result = loaded_gc_model.predict(input_text[0:5])
print(result)
```
flowingo 0.0.2
`flowingo` is `airflow` for dataflow
Requires Python: <3.10,>=3.7

# Flowingo
[![Test](https://github.com/k4black/flowingo/actions/workflows/test.yml/badge.svg)](https://github.com/k4black/flowingo/actions/workflows/test.yml)
[![Publish](https://github.com/k4black/flowingo/actions/workflows/publish.yml/badge.svg)](https://github.com/k4black/flowingo/actions/workflows/publish.yml)
[![codecov](https://img.shields.io/codecov/c/github/k4black/flowingo?token=ZX7TA177XY)](https://codecov.io/gh/k4black/flowingo)
[![pypi](https://img.shields.io/pypi/v/flowingo)](https://pypi.org/project/flowingo/)
[![docker](https://img.shields.io/docker/v/k4black/flowingo?sort=semver)](https://hub.docker.com/r/k4black/flowingo/)


<!--logo-start-->
![Flowingo](./docs/images/logo-full.png)
<!--logo-end-->


like airflow but for dataflows 


```shell
pip install flowingo
```


## Development 

See development documentation for:  

* [backend](docs/docs/developing/backend.md)  
* [frontend](docs/docs/developing/frontend.md)  
* [docs](docs/docs/developing/docs.md)