semvec/pypi-packages huggingface dataset:
spikelearn 0.1.2
Spiking neural networks for AI workflows and neuromorphic computing
Keywords: spiking neurons,neural networks,AI,neuromorphic computing,spiking neural networks,neuroscience

# Spikelearn

Implementation of spiking neural networks capable of online learning tailored
for machine learning workflows and neuromorphic computing applications.


## Motivation

We needed a SNN model with the following requirements:

- Capable of handling traditional ML workflows
- Heterogeneous, with the ability to integrate both mathematical models and
  neurons or synapses inspired on neuromorphic computing and emergent devices
- That could be easily parametrizable, in order to explore a large number of
  configurations in high performance computing environments.
- That could reproduce models in existing neuromorphic chips such as Loihi.
- That could handle neuromodulators and other neuroscience-inspired goodies.
- That could be easily extensible.
- That is capable of online learning through a variety of synaptic plasticity
  rules.


Spikelearn intends to fill that role.


## Status

Spikelearn is still in development. Please check spikelearn's
documentation in [readthedocs](https://anl-spikelearn.readthedocs.io/en/latest/index.html).


## Quick install

Through pypi:

```
pip install spikelearn
```

## Usage

```
from spikelearn import SpikingNet, SpikingLayer, StaticSynapse
import numpy as np

snn = SpikingNet()
sl = SpikingLayer(10, 4)
syn = StaticSynapse(10, 10, np.random.random((10,10)))

snn.add_input("input1")
snn.add_layer(sl, "l1")
snn.add_synapse("l1", syn, "input1")
snn.add_output("l1")

u = 2*np.random.random(10)
for i in range(10):
    s = snn(2*np.random.random(10))
    print(s)
```

## Acknowledgements

* Threadwork, U.S. Department of Energy Office of Science, 
  Microelectronics Program.


## Copyright and license

Copyright © 2022, UChicago Argonne, LLC

Spikelearn is distributed under the terms of BSD License. See 
[LICENSE](https://github.com/spikelearn/spikelearn/blob/master/LICENSE.md)

Argonne Patent & Intellectual Property File Number: SF-22-154
guess-schema-package 1.0.0
A tool for reading csv files and generating a matching Database Table in either BigQuery, Postgres or MySQL.
Requires Python: >=3.7

# guess_schema
A tool for reading csv files and generating a matching Database Table in either BigQuery, Postgres or MySQL.

### install with pip
pip install guess_schema_package

### command line test
py -m guess_schema_package.guess_schema

### Quick Start Guide
https://github.com/daveskura/postgresdave/blob/main/QuickStart.md

### Methods
https://github.com/daveskura/postgresdave/blob/main/methods.md
circuitsim-janq0 0.0.5
A small example package
Requires Python: >=3.7

# circuitsim

A simple CLI electronic circuit simulator for AC and DC linear circuits.

## Installation

```shell
mkdir circuitsim-janq0
cd circuitsim-janq0
python -m venv venv
source venv/bin/activate
pip install circuitsim_janq0
```

## Usage

```shell
circuitsim -h
```