semvec/pypi-packages huggingface dataset:
Python-Packaging-Taco-Niet-2020-BuildCLEWsModel 1
This package creates MoManI Models through Command Line Interface
Requires Python: >=3.6

This package creates MoManI models through Command Line Interface (CLI).


Note: This folder contains multiple files/folders. Depending on the type of model, ‘data’ folder would contain different Excel files. For now, it only contains an empty python file, when it is time to run the model, delete that file and copy the required Excel files into data folder.

Follow the steps below to create any MoMonI model.


Step 1: (Create a virtual environment and run all codes in there)
    * Open Command Prompt (Windows) or Terminal (Mac)
    * Go to the directory where this project is cloned/downloaded into
    * Create a virtual environment:
        * Mac: python3 -m venv venv
        * Windows: python -m venv venv
    * Activate the virtual environment
        * Mac: source venv/bin/activate
        * Windows: venv\Scripts\activate.bat
    * Install the following packages:
        * pip install pyyaml
        * pip  install pymongo
        * pip install colorama


Step 2: (Create new model on MoManI website)
    * Make sure the name you have selected for the MoManI model matches the ‘Model’ value in the Yaml file
        * example: In the yaml file, if the first line is —> Model: Model_Name, make sure name of the MoManI model is also ‘Model_Name’
    * Select ‘Create new model’
    * Choose the right Name and add Description
    * Select the following boxes:
        * OFS_Cost
        * Common_Equations 
        * Short_Code_Equations
        * Then Save
    * Now you have an empty model that will be filled with data after running the code on CLI
    * Type the following in CLI to build the model:
        * python BuildCLEWsModel.py test.yaml
        * select y --> then enter
iSSVD 0.1.5
Robust Integrative Biclustering for Multi-view Data
Requires Python: >=3.5

### iSSVD - Intergrative Biclustering for Multi-view data with nested stability selection


_Inputs_:

 **X**: A list contains multi-view data.
 
 **standr**: If True each view will to be standardized. Default: False.
 
 **pointwise**: If True a fast pointwise control method will be performed for stability selection. Default: True.
 
 **steps**: Number of subsmaples used to perform stability selection. Default: 100.
 
 **size**: Size of the subsamples used to perform stability selection. Default: 0.5.
 
 **vthr**: The proportion to be explained by eigenvalues. Default: 0.7.
 
 **ssthr**: Range of the threshold for stability selection. Default: \[0.6, 0.8\].
 
 **nbicluster**: A user specified number of biclusters to be detected. Default: 10.
 
 **rows_nc**: If True allows for negative correlation of rows over columns. Default: True.
 
 **cols_nc**: If True allows for negative correlation of columns over rows. Default: True.
 
 **col_overlap**: If True allows for columns overlaps among biclusters. Default: False.
 
 **row_overlap**: If True allows for rows overlaps among biclusters. Default: False.
 
 **pceru**: Per-comparrison wise error rate to control the number of falsely selected coefficients in the left singular vectors. Default: 0.1.
 
 **pcerv**: Per-comparrison wise error rate to control the number of falsely selected coefficients in the right singular vector. Default: 0.1.
 
 **merr**: Convergence threshold. Default: 1e-4.
 
 **iters**: Maximal iteration for detecting each bicluster. Default: 100.
 
 
_Outputs_:

 iSSVD returns the stable solutions of left singular vectors for each bicluster, and right singular vectors from each view for each bicluster. 
 
 **N**: Number of biclusters detected.
 
 **Info**: Stability selection results of left and right singular vectors.
 
 **Sample_index**: The indices of bicluster samples.
 
 **Variable_index**: The indices of bicluster variables. 
 
 **Iterations:** The interations run for each bicluster.
 
 Please check https://github.com/weijie25/iSSVD/blob/master/iSSVD/Guide.md for a simple guide.



 *Reference*

 Weijie Zhang and Sandra E. Safo. "Robust Integrative Biclustering for Multi-view Data." *Bioinformatics* submitted (2020).
kedro-grpc-server 0.1.0
Kedro gRPC Server, a Kedro plugin that creates a gRPC server for your kedro pipelines
Requires Python: >=3.6, <3.8

# Kedro gRPC Server

[![Coverage](https://img.shields.io/badge/coverage-100%25-brightgreen.svg)]()
[![Current Version](https://img.shields.io/badge/current%20version-0.1-yellow.svg)]()
[![Python Version](https://img.shields.io/badge/python-3.6%20%7C%203.7-blue.svg)]()
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)

This is a beta Kedro plugin that creates a gRPC server for your kedro pipelines. Kedro gRPC Server RPC calls can be triggered using any of the programming languages that support gRPC.

Kedro gRPC clients can be in many programming languages.

Some kedro client examples below that call RPCs on a gRPC server running in any kedro project:
- [Python]()
- [Go]()
- [Scala]()
- [Java]()
- [Node]()
- C++
- C#
- Kotlin
- Dart

More on [grpc.io](https://grpc.io)

 - Code generation support in all gRPC frameworks makes it super easy to use for clients who may have a different tech stack and language preference. Simply sharing a kedro.proto file, clients can use their preferred programming language to generate strongly typed kedro clients in any of the languages mentioned above.
 - Allowing engineers to natively integrate running of kedro pipelines with their preferred programming language.
 - Getting status of kedro pipeline run as a streaming response through HTTP/2. gRPC provides first-class support for this.
 - Getting all the benefits of gRPC
 - And, enabling business users to interact with analytics from a front-end application and trigger actions or models (e.g. scoring  model) on demand.


## How do I install Kedro gRPC Server?

### Prerequisites

Kedro gRPC Server requires [Python 3.6+](https://realpython.com/installing-python/) and [Git](https://help.github.com/en/github/getting-started-with-github/set-up-git) to be setup.

#### Installation

You can install Kedro gRPC Server directly from GitHub with:

```bash
pip install git+https://github.com/mmchougule/kedro-grpc-server.git
```

## How do I use Kedro gRPC Server?

To start the server, simply run the following command inside your Kedro project:

```bash
kedro server grpc-start
```

You can specify the host through the flag like so:

```bash
kedro server grpc-start --host
```

Similarly, you can set the port number using `--port`.

## Run

## gRPC API

Exposing 3 RPC calls similar to the REST server:

`ListPipelines` -> Returns current list of pipelines

`Run` -> Runs a pipeline with or without arguments

`Status` -> Provides run status of a pipeline with run_id.
The response for this rpc call is a Server Streaming response of all logged events.

## Contributing

Please read [CONTRIBUTING.md](CONTRIBUTING.md) for:
* The contribution code of conduct
* The process for submitting pull requests

## Versioning

We use [SemVer](http://semver.org/) for versioning. The best way to safely upgrade is to check our [release notes](RELEASE.md) for any notable breaking changes.

## What licence do you use?

See [LICENSE](LICENSE.md) for guidelines.
zohocrmapi 0.0.11
A Zoho CRM API
Requires Python: >=3.6

# Zoho CRM API
This API is for people who are having trouble with the official Zoho API. 

* Supports Zoho API v2

For full documentation visit [zoho-crm-api.readthedocs.io](https://zoho-crm-api.readthedocs.io).

## Quickstart

Install:

```
$ pip install zohocrmapi
```

Get a Client ID, Client Secret, and Grant Token from Zoho, then create a `ZohoCRMRestClient` object and generate an access token:

```python
from zohocrm import ZohoCRMRestClient

client_id = '<paste your Zoho client id>'
client_secret = '<paste your Zoho client secret>'
redirect_uri = '<paste your Redirect URL>'
grant_token = '<paste your newly created token>'
zoho_client = ZohoCRMRestClient(client_id, client_secret, redirect_uri)

zoho_client.generate_access_token(grant_token)
```

Download a Record from the API, for example a Contact:

```python
from zohocrm import ZohoCRMContact

contact_id = 1234023423424
contact = ZohoCRMContact.fetch(
    zoho_client,
    contact_id
)
```

Create a new record:

```python
contact = ZohoCRMContact(zoho_client)
contact.Last_Name = "John"
```

Update or save a Record:

```python
# no id? insert
contact.save()  # or contact.insert()

# id = <int>? update
contact.id = 12232423423
contact.save()  # or contact.update()
```

Delete a Record:

```python
# delete loaded record
contact.delete()

# delete non-loaded record from ID
ZohoCRMContact.delete_id(zoho_client, contact_id)
```
kvantin 1.3.1
Funksjoner og analyseverktøy for algoritmisk handel i verdipapirer.
Requires Python: >=3.6

# Kvantin Algorithmic Trading 

This project is not completed and all modules are not operative. 

# Overview
The Kvantin module provides the following key features:

  - Collect market information
  - Analyze

## Usage

The project is not yet completed. 

###  Getting it

To download kvantin either fork this github repo or simply install via pip.
```sh
pip install kvantin
```

License
----

MIT License

Copyright (c) 2020 Torstein Frogner Knutson 

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


Se our website: [Kvantin](http://www.kvantin.no)!