semvec/pypi-packages huggingface dataset:
secrit 0.0.5
A simple library to interact with the 'pass' (password store https://www.passwordstore.org/) generated secrets
Requires Python: >=3.7

---
title: Secrit
---

Introduction
============

Secrit is a simple Python library designed to interact with the \'pass\'
password store generated keys.

Installation
============

First, ensure that you have the \`\~/.password-store/\` directory
available as the library uses it as the default storage path.

To install the Secrit library, you can simply use pip:

``` {.bash}
pip install secrit
```

Usage
=====

Retrieve a Password
-------------------

To retrieve a decrypted content of a password store entry, use the
\`get\` function:

``` {.python}
import secrit

# You will be prompted for your master password
api_key = secrit.get("github.com/api_key")
```

Requirements
============

-   Python 3
-   python-gnupg

Development
===========

To contribute or report issues, please visit the [GitHub
repository](https://github.com/hard-simp/secrit.py).

License
=======

MIT
secret-hardsimp 0.0.2
A simple library to interact with the 'pass' (password store https://www.passwordstore.org/) generated secrets
Requires Python: >=3.7

---
title: Secrit
---

Introduction
============

Secrit is a simple Python library designed to interact with the \'pass\'
password store generated keys.

Installation
============

First, ensure that you have the \`\~/.password-store/\` directory
available as the library uses it as the default storage path.

To install the Secrit library, you can simply use pip:

``` {.bash}
pip install secrit
```

Usage
=====

Retrieve a Password
-------------------

To retrieve a decrypted content of a password store entry, use the
\`get\` function:

``` {.python}
import secrit

# You will be prompted for your master password
api_key = secrit.get("github.com/api_key")
```

Requirements
============

-   Python 3
-   python-gnupg

Development
===========

To contribute or report issues, please visit the [GitHub
repository](https://github.com/hard-simp/secrit.py).

License
=======

MIT
GPyS 0.1.2
Gaussian Process Subspace Prediction
Requires Python: >=3.10

# What is GPyS?
This is a prototypical implementation of Gaussian Process Subspace (GPS) Prediction in the Python programming language. 
For the original research article documenting the method, see the Citation section.

## Table of Contents
* [Citation](#citation)
* [Installation](#installation)
* [Example Use](#example-use)
  * [For GPS Preprocessor](#for-gps-preprocessor)
  * [For GPS Hyperparameter Training](#for-gps-hyperparameter-training)
  * [For GPS Prediction](#for-gps-prediction)

## Citation

- Ruda Zhang, Simon Mak, and David Dunson. Gaussian Process Subspace Prediction for Model Reduction. SIAM Journal on Scientific Computing, 2022. https://epubs.siam.org/doi/10.1137/21M1432739

## Installation

Install the package[^1] via pip using the following command:

- ```pip install GPyS==0.1.2```

## Example Use 

#### After installing the package you can load all modules as shown below:
```
from GPyS import GPyS_preprocessor, GPyS_prediction, GPyS_LOOCV_error
```
#### For GPS Preprocessor:
  - Note that only ```GPyS_preprocessor.Preprocessor.setup(X)``` takes in argument X and this must be called first before any other functions
  - The remaining functions merely return preprocessing quantities of interests

#### For GPS Hyperparameter Training:
  - Utilize ```GPyS_LOOCV_error.LOOCV.hSSDist(length)``` method for the objective function computation at a given (default) length scale
  - Please take a look at the LOOCV_script.py to see an example computation of optimal lengthscale for GPS. 
  - - Also, all the functions can be independently called here. 

#### For GPS Prediction:
  - Call ```GPyS_prediction.Prediction.GPS_Prediction()``` to immediately obtain prediction results
  - Also, all the functions can be independently called here. 

[^1]: this package is created and maintained by Ruda Zhang and Taiwo Adebiyi of the UQ-UH Lab.