semvec/pypi-packages huggingface dataset:
rodatabase.py 2.2.1
A Roblox Database API wrapper.
Requires Python: >=3.7

# Rodatabase.py

# How to install?

`pip install rodatabase.py`

# How to start using the Library?

```
from robase.imports import DatabaseClient

DB = DatabaseClient(universeId=YOUR_ID_HERE, token="YOUR_API_TOKEN_HERE", ROBLOSECURITY="YOUR_.ROBlOSECURITY_HERE")
```


# All functions

```
await DB.get_datastores()
Datastore = await DB.get_datastore("Test1")
await Datastore.get_keys(datastore="ExampleDatastore", limit=99)
await Datastore.set_data(datastore="ExampleDatastore", key="ExampleKey", data="ExampleData") 
await Datastore.increment_data(datastore="ExampleDatastore", key="ExampleKey", incrementby=1)
await Datastore.delete_data(datastore="ExampleDatastore", key="ExampleKey")
await Datastore.get_data(datastore="ExampleDatastore", key="ExampleKey")
```
For calling set_data data must be a JSON object.
```
All JSON objects:
    String
    Number
    JSON object
    Array
    Boolean
    null
```
test-opsramp-analytics-utils 2.4.6
OpsRamp Analytics SDK
Requires Python: >=3.6

## OpsRamp Analytics Utilities

This is the SDK for writing OpsRamp analytics apps. It is based on [dash](https://plotly.com/dash/), and it has a number of utility functions.

It contains [analysis wrapper project](https://github.com/opsramp/analysis-wrapper).

It is published on [Pypi](https://pypi.org/project/opsramp-analytics-utils/)

#### How to publish on Pypi

After make updates on SDK, modify the version in _setup.py_.

```
python setup.py sdist bdist_wheel
python -m twine upload dist/*

Note: if above command not works, then use below command
python -m twine upload --skip-existing dist/*
```

- To upgrade the sdk for your app
```
pip install --no-cache-dir --upgrade opsramp-analytics-utils
```
kmeans-sam 0.1.8
A package for creating subclusters of KMeansSam clusters and merging them with great flexibility
Keywords: k-means,sub-clustering,Merging,clustering,clusters,Machine Learning,Data Science,Python3

# k-means-sam
k-means sub-clustering and merging library for strong control and flexibility over k-means clustering

The operations provided in k-means-sam are :
1. Making k-means clusters 
2. Querying clusters by id
3. Sub-clustering k-means clusters by specifying cluster id
4. Merging k-means clusters by specifying cluster ids


**USAGE -**

### Import kmeans-sam

~~~~
import kmeans_sam.kmeans_sam as ks
~~~~

### Making clusters 

Steps - 
1. Create a k-means model and perform .fit() operations
2. Create a kmeans-sam object 
3. kmeans-sam.clusterize(model = kmeans_mode, dataframe = df, predictors = [column_names])

Code: 
~~~~
## Create data for clustering
X, _ = make_blobs(n_samples=10, centers=3, n_features=4)
df = pd.DataFrame(X, columns=['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4'])
## Create KMeans model
kmeans = KMeans(n_clusters=3)
kmeans.fit(df[['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4']])
## Create KMeansSam object
kmeans_sam = ks.KMeansSam()
## Create KMeansSam clusters. parameters : kmeans model, dataframe, list of column names
clusters = kmeans_sam.clusterize(kmeans, df, ['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4'])
print(clusters)
~~~~


### Querying cluster by id

Code:
~~~~
## Fetched all the clusters with id
clusters = kmeans_sam.get_all_clusters()
print(kmeans_sam.get_cluster(2))
~~~~

### Sub-clustering k-means clusters by specifying cluster id

~~~~
kmeans = KMeans(n_clusters=2)
kmeans.fit(df[['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4']])
## parameters : kmeans model, cluster_id, list of column names
print(kmeans_sam.subclusterize(kmeans, 0, ['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4']))
~~~~

### Merging clusters by cluster_ids
Code:
~~~~
print(kmeans_sam.merge_cluster([0, 2]))
~~~~
extract-mailchimp 0.0.8
Extract MailChimp data only by folder ID
Requires Python: >=3.6
Keywords: data,api,mailchimp

## MailChimp Extractor by Directory ID
---

Download mailchimp campaign data by campaign directory (folder) id
Origin by [mailchimp-marketing-python](https://github.com/mailchimp/mailchimp-marketing-python)

### Requirements
---
Python 3.6+

### Installation 
#### pip install
```python
pip install extract-mailchimp==0.0.7  
```

### Endpoints

|                Method                |     requirement      |    return     |
|:------------------------------------:|:--------------------:|:-------------:|
| `get_campaign_raw_data_by_folder_id` | {campaign_folder_id} |     JSON      |
|    `get_campaign_id_by_folder_id`    | {campaign_folder_id} |     list      |
|   `campaign_information_to_pandas`   |    {campaign_id}     | pd.DataFrame  |
|      `click_details_to_pandas`       |    {campaign_id}     | pd.DataFrame  |

### Usage

you must make `.env` file with your mailchimp api key and server

```python
from extract_mailchimp import MailChimp

if __name__ == "__main__":
    campaign_folder_id = {campaign_folder_id}
    mc = MailChimp()
    print(mc.get_campaign_id_by_folder_id(campaign_folder_id))
    # [['{campaign_id}', '{campaign_title}']]
```
RhcsQeSdk 0.0.7
RHCS QE SDK Library

## Introduction

RH Ceph QE Focus Groups mainly contribute test coverage features in the cephci repository. Test automation requires methods that could execute a specific workflow, verify the result and post this information to the user. At present, a QE engineer creates methods inside their own workspace. The implemented method has a high probability that it is required by another team. For example: a method to restart a specific Ceph Daemon.

These methods are generally development keeping the FGs objectives in mind. This makes it less attractive if another team wants to reuse them. This causes an explosion in the methods being create in the repository.

# Getting Started 

RHCS QE SDK for Python can be installed from Source, Pypi installation methods.

   ## From Source
   ```bash
   $ git clone https://gitlab.cee.redhat.com/rhcs-qe/rhcs-qe-sdk.git
   $ cd rhcs-qe-sdk 
   ```
   ```python
   $ python setup.py install --user  # to install in the user directory (~/.local)
   ```
   ```bash
   $ sudo python setup.py install    # to install globally
   ```
   
   ## Or using PIP:
   ```bash
   $ git clone https://gitlab.cee.redhat.com/rhcs-qe/rhcs-qe-sdk.git
   $ cd rhcs-qe-sdk 
   ```
   ```python
   $ pip install . 
   ```   
   
   ## From Pypi
   ```bash
   $ git clone https://gitlab.cee.redhat.com/rhcs-qe/rhcs-qe-sdk.git
   $ cd rhcs-qe-sdk
   ```
   ```python
   $ pip install RhcsQeSdk
   ```

## Install rhcs-qe-sdk's dependencies
   ```python
   $ pip install -r requirements.txt
   ```
octavia-proxy 1.1.1
Open Telekom Cloud Octavia API proxy
Keywords: OpenStack, openstackclient, openstacksdk, Open Telekom Cloud, otc, T-Systems, Telekom

=============
Octavia Proxy
=============

This project translates Octavia API requests into elbv2 and elbv3 of the Open
Telekom Cloud. It is required due to missing possibility to implement elbv3
support as a native Octavia driver (it doesn't offer admin type of access and
to have primary data source in Octavia).

Goals
-----

- provide customer facing load balancer APIs using Octavia API
- cover elbv3 and elbv2
- Admin APIs are not going to be implemented

Status
------

POC in development phase

Requirements
------------

- validatetoken (fork of keystonemiddleware.auth_token middleware to
  allow token validation in the frontend)
- python-otcextensions (elb branch)

Developer setup
---------------

- tox create venv for octavia-proxy
- source into it
- with the venv python go to otcextensions elb branch and do
  `python setup.py develop`
- add into the clouds.yaml
  `load_balancer_endpoint_override: http://127.0.0.1:9876/`.
  IMPORTANT (for now): do not use profile:otc
- get python-openstackclient with python-octaviaclient (otce overrides
  loadbalancer function now, therefore - upstream)
- `python octavia_proxy/cmd/api.py --config-file etc/octavia.conf`
- `openstack loadbalancer list`
TriGeneI 0.1
TGMI network inference using Python
Requires Python: >=3

# TGMI
Rewrite TGMI network inference using Python

## example
Download and enter into the TGMI directory
~~~shell
cd TGMI
python TGMI.py -i  example/expression.txt -t example/tfs.txt -g example/genes.txt -o tgmi.txt
~~~
input file format see [example](./example)  

There will be three outputs:
1. tgmi_network.txt  
2. tgmi_TF.txt
3. tgmi_Gene.txt

## Reference
Chathura Gunasekara, Kui Zhang, Wenping Deng, Laura Brown, Hairong Wei, TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction, Nucleic Acids Research, Volume 46, Issue 11, 20 June 2018, Page e67, https://doi.org/10.1093/nar/gky210