semvec/pypi-packages huggingface dataset:
CGPCLI 1.1.9
Package for CGP CLI access
Requires Python: >=3.6

CGPCLI is Python libary that allows you to reach CGP servers and execute CLI commands from your Python code.

It consists of:
- **Commands**: contains all CLI command
- **Errors**: some errors with that you may handel
- **Connection**: methods to connect to CGP server
- **Parser**: includes methods to convert CGP objects to Python ones and vice versa

[CLI commands manual](http://www.communigate.ru/CommuniGatePro/CLI.html)

Simple example:
```
  server = Server('hostname')
  server.connect()
  server.login('username', 'password')
  print(server.get_account_info('*'))
  server.disconnect()
```
multicubepy 0.0.2
A python library that works along MultiCube. It allows users to manipulate FodoMust using Python code.
Requires Python: >=3.6

# MultiCube-py

## Pre-condition
This library works aside the MultIcube tool (The GUI for FodoMust).

## Functionalities
This is a demo version, it only allows us to fetch the clustering result and the initial data.

## Manual
    After terminating a clustering task which is launched through MultIcube:

    Configure the env_path of MultiCube:
        > cd path-to-muticube  

        > export MULTICUBE_HOME=$PWD

    Run your script that imports MultiCube-Py.

## Example

```Python
import multipy as multipy
import pandas as pd

multipy.launch()
data_interface = multipy.get_data_of(1)
result = multipy.get_classification_result(1)


data = data_interface.get("_data")
copy_data = list()
for obj in data:
    new_obj = list()
    for val in obj:
        new_obj.append(int(val[0]))
    copy_data.append(new_obj)

attribute_names = data_interface.get("attributeName")
df = pd.DataFrame(copy_data)
df = df.set_axis(attribute_names, axis=1, inplace=False)
df["cluster_map"] = result.get("clusterMap")
print(df)

```