semvec/pypi-packages huggingface dataset:
panoramic-auth 1.0.0
Panoramic Authentication SDK
Requires Python: >=3.6
Keywords: panoramic,authentication,sdk

# Panoramic Auth SDK

## Getting Started

This SDK handles the access token generation using client_credentials flow against Panoramic. To install the SDK in your app, add it to your requirements.txt:

    panoramic-auth==1.0.0

All you then need to do is subclass the OAuth2Client Python class from the SDK, pass your client_id and client_secret, and use the session attribute from your subclass when making HTTP requests:

```py
# Import the class as usual
from panoramic.auth import OAuth2Client

# Note subclassing OAuth2Client below
class PanoramicApiClient(OAuth2Client):
    def __init__(self, base_url: str, client_id: str, client_secret: str):
        super().__init__(client_id, client_secret)
        self.base_url = base_url
   
    def async_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        url = urljoin('https://platform.panoramichq.com/api/example')
        # Note self.session below
        response = self.session.post(url=url, json=data)
        response.raise_for_status()
        return response.json()
```

## Development

This repository does not have a dedicated docker image. At the moment, we create python virtual environment using command (in directory `.venv` inside current directory):

```
> python3 -m venv .venv
```

If you use pyenv and pyenv-virtualenv, you can create it using:

```
> pyenv virtualenv panoramic-auth
```

Then, you can switch to it from command-line using following command:

```
> source .venv/bin/activate
```

Or if using pyenv-virtualenv:

```
pyenv local panoramic-auth
```

Lastly, use following command to install dependencies (make sure you have correct python environment active):

```
> make install
```

Install pre-commit - useful to avoid commiting code that doesn't pass the linter:

```
> make pre-commit-install
```

This installs git hooks that run pre-commit.

## Tests

Use following command to run all tests:

```
> make tests
```

## Release process

To release a new version of the library, follow these steps:

* In your PR, update version in [setup.py](setup.py) and add entry to [CHANGELOG.md](CHANGELOG.md)
* After merge, tag the commit with version number from setup.py. For example `git tag v0.1.1`. You can also do this by creating a new [release](https://github.com/panoramichq/panoramic-auth-py/releases).
* This triggers a Travis pipeline which runs tests, linters and uploads the package to [PyPI]()
raptorfinance 0.0.1
finance api especially for Indian NSE/BSE Stock Data

# Yahoo Finance Python API

# This API Supports to retrieve data especially works for NSE/BSE stocks.
# Also can Retrieve Intraday 1min gradualar Data.

## Usage

This command returns a dataframe which can be further modified to add new columns , exported to excel etc

``` python
hdfc = RaptorFinance('HDFC.NS', result_range='1mo', interval='15m', dropna='True').result
```


## Installation

``` bash
git clone https://github.com/swaroop9ai9/Fintech/raptor_finance_api.git
cd raptor_finance_api
python setup.py install
```

### Requirements

- Pandas
- Request

### Note

Make sure to use TICKER symbol from yahoo finance website
https://in.finance.yahoo.com/
raptor-finance-api 0.0.1
finance api especially for Indian NSE/BSE Stock Data

# Yahoo Finance Python API

# This API Supports to retrieve data especially works for NSE/BSE stocks.
# Also can Retrieve Intraday 1min gradualar Data.

## Usage

This command returns a dataframe which can be further modified to add new columns , exported to excel etc

``` python
hdfc = RaptorFinance('HDFC.NS', result_range='1mo', interval='15m', dropna='True').result
```


## Installation

``` bash
git clone https://github.com/swaroop9ai9/Fintech/raptor_finance_api.git
cd raptor_finance_api
python setup.py install
```

### Requirements

- Pandas
- Request

### Note

Make sure to use TICKER symbol from yahoo finance website
https://in.finance.yahoo.com/
multilayer-credit-scoring 0.0.7
Python implementation of the MultilayerCreditScoring algorithm
Requires Python: >=3.8

# MultilayerCreditScoring

This repository includes a python script that implements the MultilayerCreditScoring (MCS) algorithim presented in the paper [Evolution of Credit Risk Using a Personalized Pagerank Algorithm for Multilayer Networks](https://arxiv.org/abs/2005.12418).

# Usage instructions

What is supported regarding the format of the input data:

to be continued...

Usage and the parameters the user can control

1. Filename for input data
2. Network_type, defaUlt is bipartite multilayer but mulitplex can also be specified !?! //TODO: check this further
3. Alpha parameter for the personalized pagerank algorithm. Default is 0.85

We will do a MCS class that takes all the neccassary parameters in its contructor and it will run the caluclatinson directly afterwards. Then the user can query the results he is interesed in by examaning the relevant properites of the MCS object.
p3elf 0.1.7
A tiny python3 package for parsing ELF files
Requires Python: >=3.5

# **p3elf**

A tiny python3 module for parsing ELF file metadata that I'm writing to familiarize myself with python binary IO as well as to gain more python experience.

Currently, it can: 

* parse the ELF file header

* parse program headers and section headers

* dump segments

* dump sections

## TODO
* cli tool