semvec/pypi-packages huggingface dataset:
gpcsd 1.0.2
Gaussian process current source density estimation
Requires Python: >=3.7, <4

# GPCSD (Gaussian process current source density estimation)

Python implementation of Gaussian process current source density (CSD) estimation.

Paper: Klein, N., Siegle, J.H., Teichert, T., Kass, R.E. (2021) Cross-population coupling of neural activity based on Gaussian process current source densities. (preprint: https://arxiv.org/abs/2104.10070)

This code estimates current source density over space and time from local field potential (LFP) recordings.

Full source code available at https://github.com/natalieklein/gpcsd.

## Installation
Install using `pip install gpcsd`. 
To get the scripts to reproduce the paper results, get the source code from github: https://github.com/natalieklein/gpcsd.
Dependencies should be automatically installed by `pip`, but also see the Anaconda `environment.yml` file.
It will install two github-based dependencies with `pip` that are not able to be included in `setup.py`.

## Main source code
Directory `src/gpcsd` contains the main source code. There are classes `gpcsd1d.py` and `gpcsd2d.py` for the GPCSD models, in addition to some support functions in other files.

## Simulation studies
Simulation studies are found in `simulation_studies` and reproduce all simulation results shown in the paper. See `simulation_studies/README.md` for a full description of the scripts.

## Auditory LFP analysis
Here we apply GPCSD1D to two-probe auditory cortex LFPs measured in a macaque monkey. The scripts reproduce all results shown in the paper. See `auditory_lfp/README.md` for more information on the scripts.
The auditory LFP data can be downloaded from https://doi.org/10.5281/zenodo.5137888, or using script `download_data.sh`.
The code assumes that it will be downloaded into `auditory_lfp/data/`. 

## Neuropixels analysis
We apply GPCSD2D to LFP recordings from Neuropixels probes in a mouse. This reproduces the figures shown in the paper. See `neuropixels/README.md` for more detail.
The Neuropixels data can be downloaded from https://doi.org/10.5281/zenodo.5150708, or using script `download_data.sh`.
The code assumes that it will be downloaded into `neuropixels/data/`.

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5154196.svg)](https://doi.org/10.5281/zenodo.5154196)
HandyBlackjack 0.0.1
A Single-Deck Blackjack hand generator and calculator

# HandyBlackjack

<p>
    <a href="https://github.com/antogno/handyblackjack/blob/master/LICENSE"><img src="https://img.shields.io/github/license/antogno/handyblackjack" alt="License"></a>
    <a href="https://github.com/antogno/handyblackjack/commits"><img src="https://img.shields.io/github/last-commit/antogno/handyblackjack" alt="Last commit"></a>
    <a href="https://github.com/antogno/handyblackjack/releases/latest"><img src="https://img.shields.io/github/v/tag/antogno/handyblackjack?label=last%20release" alt="Last release"></a>
</p>

HandyBlackjack is a Single-Deck Blackjack hand generator and calculator.

---

## Installation

Use the package manager [Pip](https://pip.pypa.io/en/stable/) to install HandyBlackjack.

```console
pip install HandyBlackjack
```

## Usage

```python
import HandyBlackjack
```

```python
HandyBlackjack.generate_cards()
# Returns two random cards with their value.
# For example:
# {
#     'card_one': {
#         'value': '10',
#         'suit': 'H',
#     },
#     'card_two': {
#         'value': '4',
#         'suit': 'S',
#     },
#     'points': 14
# }
```

```python
HandyBlackjack.calculate_points(['A', '6'])
# Return: 17
```

## License

HandyBlackjack is licensed under the terms of the [Creative Commons Zero v1.0 Universal license](https://github.com/antogno/handyblackjack/blob/master/LICENSE).

For more information, see the [Creative Commons website](https://creativecommons.org/publicdomain/zero/1.0/).
jlab-jaws 2.1.0
Jefferson Lab Alarm Warning System API
Requires Python: >=3.6

# jaws-libp
Reusable Python Classes for [JAWS](https://github.com/JeffersonLab/jaws).

## Install
```
pip install jlab-jaws
```

## API
[Sphinx Docs](https://jeffersonlab.github.io/jaws-libp/)


## See Also
 - [Developer Notes](https://github.com/JeffersonLab/jaws-libp/wiki/Developer-Notes)
borgo 0.0.7
Library for sending requests to the Borgo app.
Requires Python: >=3.6

# Borgo

Borgo is an [API](https://pypi.org/project/borgo) + [App](https://borgo.app) for easily interfacing with your python scripts.

If your script needs human input, create a borgo request, and it will be sent to your mobile app. Your script will wait until you act on the request, and then continue with the results.

For example:

```python
from borgo import *

borgo_key = '''
eyJhbGciOiJSUzI1NiIsIOTYwYjA1Yzk3ZmE0MDljNDdhNDQ0ZTciLCZXRva2VuLmdvb2dsZSyZ28...
'''

with BorgoClient(borgo_key) as borgo_client:
  request = BorgoRequest('Should I tweet "hello world"?', 'Tweet', "Don't tweet")
  response = borgo_client.wait_for(request)

  if (response.is_accepted):
    # Send "hello world" to Twitter API.
```

## Getting a Key

To get a key to pass into the `BorgoClient(...)` constructor, sign in to [borgo.app](https://borgo.app), click on the "key" icon, hit the button to copy the key to your clipboard and then paste it as a string into your script.

## Community

For questions and discussion about Borgo, visit [reddit.com/r/borgo](https://www.reddit.com/r/borgo)