semvec/pypi-packages huggingface dataset:
configloader-Marasga 0.0.1
json-based config handler
Requires Python: >=3.6

# configlaoder Package

This is a simple config loader package. 
It is based on the idea of saving configuration file for
python program in a json and loading it into an object.
You can use
[Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content.
blockchain-etl-table-definition-cli 1.3.0
Tools for generating table definitions for https://github.com/blockchain-etl/ethereum-etl-airflow
Requires Python: >=3.5.3,<4
Keywords: ethereum,polygon,bsc,arbitrum,fantom,celo,ronin,avalanche

# Blockchain ETL Table Definition CLI

Blockchain ETL Table Definition CLI allows generating table definitions for 
[Ethereum ETL](https://github.com/blockchain-etl/ethereum-etl-airflow/tree/master/dags/resources/stages/parse/table_definitions),
[Polygon ETL](https://github.com/blockchain-etl/polygon-etl/tree/main/airflow/dags/resources/stages/parse/table_definitions),
[EVM Chain ETL](https://github.com/nansen-ai/evmchain-etl-table-definitions/tree/main/parse),
[Solana ETL](https://github.com/nansen-ai/solana-etl-table-definitions/tree/main/parse).

Read this article for more details: [How to get any Ethereum smart contract into BigQuery (in 8 mins)](https://towardsdatascience.com/how-to-get-any-ethereum-smart-contract-into-bigquery-in-8-mins-bab5db1fdeee).

## Requirements:

- Python 3.6+

## Quickstart

Install the cli:

```bash
pip install blockchain-etl-table-definition-cli
```

Generate table definitions (`example_uniswap_abi.json` can be downloaded from [here](https://github.com/blockchain-etl/ethereum-etl-table-definition-cli/blob/main/example_uniswap_abi.json)):

```bash
tabledefinition generate \
    --abi-file example_uniswap_abi.json \
    --dataset-name uniswap \
    --contract-name Uni \
    --contract-address 0x1f9840a85d5af5bf1d1762f925bdaddc4201f984
```

For Solana:

```bash
tabledefinition generate \
    --chain solana \
    --abi-file example_metaplex_idl.json \
    --dataset-name metaplex \
    --contract-name AuctionHouse \
    --contract-address hausS13jsjafwWwGqZTUQRmWyvyxn9EQpqMwV1PBBmk
    --include-functions
```

Output will be in the `output` directory.

---

For the latest version, check out the repo and call 

```bash
pip install -e .
python tabledefinition.py --help 
```
ANI1datatools 0.0.3
package used to access the ANI-1 data set
Requires Python: >=3.6

# ANI-1 dataset support repository
This repository contains the scripts needed to access the ANI-1 data set. The structure of this repository has been updated so the tools can be installed easily using pip. This modified repository is a fork of https://github.com/isayev/ANI1_dataset created by [Olexandr Isayev](https://github.com/isayev) 

##### If you use ANI-1 dataset please cite the following two papers: 

Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg. *ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost.* Chemical Science, 2017, DOI: 10.1039/C6SC05720A 

Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg. *ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules.*  Scientific Data, 4, Article number: 170193, DOI: 10.1038/sdata.2017.193 https://www.nature.com/articles/sdata2017193

### Required software
Python3.5 or better
Numpy
H5PY

### Included extraction software
pyanitools.py
	-Contains a class called 
	 "anidataloader" for loading
	 and parsing the ANI-1 data set.

example_data_sampler.py
	-Example of how to sample data
	from the anidataloader class.

### Installation instructions

1) run `pip install ANI1datatools`

### Description
The downloaded file (https://doi.org/10.6084/m9.figshare.c.3846712) can be extracted on a Unix based system with the “tar -xzf ani-1_dataset.tar.gz” command. Once extracted, a folder named “ANI-1_release” is the root directory for all files. The individual data files are separated into 8 HDF5 files (extension .h5) named ani_gdb_s0x.h5 where x is a number between 1 and 8 representing the number of heavy atoms (CNO) in the molecules contained in the file. The README file contains information about the data set and scripts included. The folder named “readers” has a code sample for reading the HDF5 file called “example_data_sampler.py” and “lib/pyanitools.py”, which contains classes for loading and storing data in our in-house format.
File format

The ANI-1 data set is stored in the HDF5 [http://www.hdfgroup.org/HDF5] file format. Two python classes are included with the data set’s compressed archive in the python file: “ANI-1_release/readers/lib/pyanitools.py”. These classes are only tested for python version 3.5 and greater, and requires the h5py library [http://www.h5py.org/]. An example script for reading the data from the HDF5 files is given in: “ANI-1_release/readers/example_data_sampler.py”.

### Data Units
Coordinates: Angstroms
Energies: Hartrees

### Self-interaction atomic energies
H = -0.500607632585
C = -37.8302333826
N = -54.5680045287
O = -75.0362229210

## Related work

### Developing the ANI-1x potential with active learning:
Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E. Roitberg. *Less is more: sampling chemical space with active learning*. arXiv, 2018, DOI: [arXiv:1801.09319] (https://arxiv.org/abs/1801.09319)