semvec/pypi-packages huggingface dataset:
eth-blocky 0.2.2
Ethereum utilities for mapping blocks to timestamps

# eth-blocky
[![Build Status](https://vonum.semaphoreci.com/badges/eth-blocky.svg)](https://vonum.semaphoreci.com/projects/eth-blocky)
[![PyPI version](https://badge.fury.io/py/eth-blocky.svg)](https://badge.fury.io/py/eth-blocky)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

Package providing date utilities for finding blocks.

With `eth-blocky`, you can:
1. Find the closest block to a given timestamp that happened before
2. Find the closest block to a given timestamp that happened at the exact same
   time or after

### Installation
1. `pip install eth-blocky`

### Usage
Provide a timestamp for which you want to find the closest block, and as a
result you will get the block object. For more information check out [web3
docs](https://web3py.readthedocs.io/en/stable/)

```Python
from eth_blocky import EthBlocky

client = EthBlocky(NODE_URL)

# 2021-03-11T12:00:00
timestamp = 1615464000

block = client.closest_block(timestamp, before=True)
print(block.number)
# 12017166

block = client.closest_block(timestamp)
print(block.number)
# 12017167
```

### Method
`eth-blocky` works as an optimized binary search.
Start with finding the first and latest block as the boundry. Left and right
block.

Steps:
1. Get left and right block and their timestamps
2. Calculate the average time for blocks to being mined in this time range
3. Estimate distance from start block based on timestamps [1, 3, 10] -> 0.3
4. Estimate wanted block based on distance
5. Calculate potential error
6. Repeat from step 1 with [estimated block - error, estimated block + error] as
   the left and right block

The process is repeated until left block is equal to right block.
SRPAstro.STATS 1.5.0
Statistical tools under SRP
Keywords: astronomy data analysis

# Swift Reduction Package

Background
The Swift Reduction Package (hereafter SRP) is a packet of command line tools to solve problems (e.g. basic reduction and analysis tasks of optical/NIR astronomical data, quick cosmological computations, units conversions, etc.) often met in astronomical research activities.

SRP was originally developed in the context of the Swift follow-up activities of the Milan GRB team at the INAF/Brera Astronomical Observatory. The package is designed to be an aid to any researcher to drive further observation of a followed-up GRB counterpart and “swift” can therefore be read simply as “rapid”, “agile”, etc.
 
 This subpackage if the SRP suite is devoted to statistical tools of common use.
nmr-sims 0.0.6
A package for running basic NMR simulations.
Requires Python: >=3.8

[![PyPI badge](https://badge.fury.io/py/nmr-sims.svg)](https://pypi.org/project/nmr-sims)

# nmr\_sims

NMR simulations in Python.

Some rather simple simulations of basic NMR pulse sequences.

Everything uses the Zeeman basis in Hilbert space, so nothing too fancy is possible.

Currently have pulse-acquire, 2DJ, and HSQC code available.

Install with `$pip install nmr-sims`

To get started, take a look at (and run) `$python -m nmr_sims pa`, `$python -m nmr_sims jres` or `$python -m nmr_sims hsqc`.

[DOCUMENTATION](https://foroozandehgroup.github.io/nmr_sims/content/)