semvec/pypi-packages huggingface dataset:
waxNFTdispatcher 0.3.6
This library will help you to transfer or to mint NFTs on the WAX blockchain
License: MIT
Requires Python: <4.0,>=3.8.0

# waxNFTdispatcher

This library will help you to transfer or to mint NFTs on the WAX blockchain. It relies on the library
pyntelope for blockchain interaction and on the library loguru for beautiful logs.

By default, the [EOSUSA](https://eosusa.io/) WAX API is used to post transactions to blockchain.
It can be changed while creating an object.
Due to some limitations only EOSUSA API can be used to get transaction info from blockchain.


## Installation
```poetry add waxNFTdispatcher```

or

```pip install waxNFTdispatcher```

## Usage

```python
from waxNFTdispatcher import AssetSender
import os

private_key = os.environ["PRIVATE_KEY"]
collection_wallet = "mywallet.wam"
collection = "pixeltycoons"
recipient = "recipient.wam"
INPUT = (("rawmaterials", "318738"), ("magmaterials", "416529"))

# Create object
assetsender = AssetSender(collection, collection_wallet, private_key)

# Try to find assets in the collection wallet to send them.
# If not all needed assets were in the collection wallet, the script will mint the rest.
assetsender.send_or_mint_assets(INPUT, recipient)

# Send assets with given asset ID to the given wallet
assetsender.send_assets(("1099543811405", "1099543811406"), recipient)

# Mint given number of same assets
assetsender.mint_assets("rawmaterials", "318738", "recipient.wam", 5)

# Mint given number of same assets and then try to fetch their IDs
assetsender.mint_assets_and_get_ids("rawmaterials", "318738", "recipient.wam", 5)
```

The methods return tuple or list of tuples where on the first place is the asset ID or id-schema-template tuple, and
on the second place either hash of successful transaction or 'False' if transaction failed for some reason. For example:

```
[(('1099511811820', 'rawmaterials', '318738'), False),
(('1099511811819',), '6b80b145aa261736941583ed17802a8be0254cd21a78b6bb415c923ec64ad32c')]
```

## Contribution
Contribution is highly welcome. Please send your pull requests or create issues with found bugs and suggestions.
In your pull requests please use Black formatting.

## Donation
If you feel like you can send any amount of WAX to the WAX address 24.gm
nessai-models 0.5.0
Models for nessai
License: MIT
Requires Python: >=3.8
Keywords: nested sampling, normalizing flows, machine learning, nessai

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7105559.svg)](https://doi.org/10.5281/zenodo.7105559)
[![PyPI](https://img.shields.io/pypi/v/nessai-models)](https://pypi.org/project/nessai-models/)
[![Integration tests](https://github.com/mj-will/nessai-models/actions/workflows/integration-tests.yml/badge.svg)](https://github.com/mj-will/nessai-models/actions/workflows/integration-tests.yml)
[![Unit tests](https://github.com/mj-will/nessai-models/actions/workflows/tests.yml/badge.svg)](https://github.com/mj-will/nessai-models/actions/workflows/tests.yml)

# nessai-models

Models for use with the nested sampling package [`nessai`](https://github.com/mj-will/nessai).

## Included models

* n-dimensional unit Gaussian
* n-dimensional HalfGaussian
* n-dimensional Rosenbrock
* n-dimensional mixture of Gaussians
* n-dimensional slab plus spike model
* Gaussian mixture using data to based on [this example](https://github.com/johnveitch/cpnest/blob/master/examples/gaussianmixture.py) from `cpnest`
* n-dimensional Egg Box based on the version in [Feroz et al. 2008](https://arxiv.org/abs/0809.3437)
* n-dimensional Pyramid-like model
* n-dimensional Brewer likelihood (Skilling's "Staistical Model") from [Brewer et al.](https://arxiv.org/abs/0912.2380)
* Linear signal plus Gaussian noise model (`LinearSignal`)
* Sinusoidal signal plus Gaussian noise model (`SinusoidalSignal`)
* Mixture of 1-dimensional distributions (`MixtureOfDistributions`)

## Requirements

`nessai_models` requires:

* `numpy`
* `scipy`
* `nessai>=0.6.0`

## Installation

> We recommend following the [installation instructions for `nessai`](https://github.com/mj-will/nessai#installation) and then installing `nessai_models` since it shares all of its dependencies with `nessai`.

`nessai_models` can be install from PyPI using

```console
pip install nessai-models
```

## Example usage

Below is an example of using `nessai_models` so configure a 4-dimensional Gaussian and then sample it using `nessai`.

```python
from nessai import FlowSampler
from nessai_models import Gaussian

model = Gaussian(4)
fs = FlowSampler(model, output='example/')
fs.run()
```

## Citing

If you use `nessai_models` in your work please cite the [Zenodo DOI](https://doi.org/10.5281/zenodo.7105559)