semvec/pypi-packages huggingface dataset:
ldv 0.1.5
Light Data Versioning
Requires Python: >=3.10

# ldv
Light Data Versioning

Ldv is a tool for version tracking big files on a remote storage and storing digest files together with code that uses the big files.
A digest file is a small file containing information about the file to version track. The information includes file size in bytes, timestamp of version, hexdigest (the version number), and local and remote filepaths.

Ldv can be run from the command line or from code, using the APIs of the package.

Ldv has no external dependencies on other programs, e.g. Git.

Currently, only AWS is supported as remote storage.

# Getting started

## Installation
This will install the package to version track on AWS remote storage.
```
pip install ldv
```

# Usage

## 1. Authentication
Authenticate with remote storage provider.

```
ldv auth aws -rn <region_name> [-pn <profile name>] [-ec]
```
* -rn is the region name where the bucket is located.
* -pn is the name of the profile specified in ~/.aws/config
* -ec Use credentials stored in environment variables. LDV_AWS_ACCESS_KEY_ID and LDV_AWS_SECRET_ACCESS_KEY must be set. Will not be used if profile is provided.

You either use -pn or -ec, not both.

Example:
```
ldv auth aws -rn eu-west-1 -pn dev-profile
```

## 2. Initialize
Initialize the local base path you want to version track files under. Also specify the remote storage url.

```
ldv init -p <path> -u <url> [-rta] [-du]
```
* -p specifies the local path that contains files to version track.
* -u is the remote storage url, e.g. s3://my_bucket/sub_folder.
* -rta is an optional flag will run version tracking of all files under the path.
* -du is an optional flag will prevent files from being uploaded to remote storage every time you version track them. Use this if you only want to create a digest files locally but not upload the files to remote storage.

Example:
```
ldv init -p local-data-folder -u s3://my_s3_bucket/sub_folder
```



## Version track file
```
ldv versioning track <file path> [-du]
```

* file path to version track. Can be absolute or relative path but must be under path provided when running 'init' command.
* -du is optional flag if you don't want to upload the file.

Example:
```
ldv versioning track local-data-folder/first_file.txt
```
## Version track all files
Version track all files under path specified in the 'init' command.
```
ldv versioning track-all [-du]
```
* -du is optional flag if you don't want to upload the files.

## Download file
Download file from remote storage to local storage.
```
ldv versioning download <file path to digest file> [-v <version>]
```
* -v is the optional version. If not provided, the version in the .digest file will be used.

## Download all files
Download all files under folder specified in 'init' command.
```
ldv versioning download-all
```

## List versions of file
```
ldv versioning list-versions <path to digest file>
```

Example:
```
ldv versioning list-versions local-data-folder/first_file.txt.digest
```

# Contact
Martin Neumann, ldv@martinneumann.se
bloghunch 0.1.0.1
A Python SDK for the Bloghunch API
Requires Python: >=3.7

# Bloghunch Python SDK

This is a Python SDK for interacting with the Bloghunch API. It provides an easy-to-use interface for accessing various Bloghunch features.

## Installation

You can install the Bloghunch python SDK using pip:

```
pip install bloghunch
```

## Usage

Here's a quick example of how to use the bloghunch SDK:

```python
from bloghunch import Bloghunch

# Initialize the Bloghunch client
client = Bloghunch(key="your_api_key", domain="your_domain")

# Get all posts
posts = client.get_all_posts()
print(posts)

# Get a specific post
post = client.get_post("post-slug")
print(post)

# Get comments for a post
comments = client.get_post_comments("post_id")
print(comments)

# Get all subscribers
subscribers = client.get_all_subscribers()
print(subscribers)

# Get all tags
tags = client.get_all_tags()
print(tags)
```

## Features

- Get all posts
- Get a specific post by slug
- Get comments for a post
- Get all subscribers
- Get all tags

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
epimodel-sensitivity-test 0.1.28
Efficient sensitivity analysis and evaluation of epidemiological models

# Sensitivity analysis of epidemic models
This repository was created during the making of the Bsc thesis ["Sensitivity analysis of
age-specific vaccination in epidemic modelling"](https://drive.google.com/file/d/1ICZ_JTFdJ_zOCKJDFukfIcOrxkguWCFT/view?usp=sharing)(Hungarian) by K. Kovács, Zs. Vizi and B. Péter. It serves as a
starting point for the sensitivity analysis of epidemic models. Colab notebook with examples can be found [here](https://drive.google.com/file/d/1JpfBjiywkaVfUW8NWO7YCb95ZAxBGWZ7/view?usp=sharing)

## Introduction
The purpose of this project is to provide a general framework for the sensitivity
analysis of deterministic compartmental epidemic models, as well as a tool for predictive modeling.
Sensitivity analysis is performed using Latin Hypercube Sampling (LHS) for parameter sampling
and Partial Rank Correlation Coefficient (PRCC) as a metric for sensitivity. The pipeline is fully
compatible with CUDA and provides an efficient representation of the epidemic model used, enabling
parallel evaluation based on different parameter combinations and initial values.

For evaluation of the model we use the package `torchode`, and we represent the ODE system corresponding
to the model with matrix operations.

## Pipeline
The user shall provide the following:
- Model parameters
- Model structure
- Simulation config
- Population distribution
- Contact matrix

The exact format of the inputs are specified in the documentation.

The following flowchart represents the pipeline established in this project: ![Flowchart](/images/Flowchart.jpg)

The LHS tables as well as the outputs are saved in subfolders in the folder `sens_data`.