semvec/pypi-packages huggingface dataset:
temporal-lib 0.1.2
A library for working with datetime and other temporal concepts.
License: MIT
Requires Python: >=3.9
Keywords: datetime, cron

### temporal-lib
A library for working with datetime and other temporal concepts.

### Explanation
Originally, I created a "Temporal" [companion App](https://github.com/Datahenge/temporal) for the [Frappe Framework](https://frappeframework.com/).
Over time, more of my non-ERPNext Python projects required or benefitted from the same helper functions and classes.

So I'm splitting the project:
* All generic, reusable Python code will be stored in this package, and synchronized to PyPi.org.
* The remaining Frappe-specific code will remain on GitHub, and begin to reference this package as a requirement.

### Installation
```bash
pip3 install temporal-lib
```

### Usage
The library's namespace is `temporal_lib`.  For example:

```python
""" your_code.py """
from datetime import datetime
import temporal_lib

today_date = datetime.now().date()
today_iso_string = temporal_lib.date_to_iso_string(today)
print(today_iso_string)
```
----

### Standards

* All date strings are expected to be ISO-8601 extended.  Example: 2023-11-29

### Links
* To the `temporal-lib` package on PyPi [here](https://pypi.org/project/temporal-lib/).
* A helpful [Wikipedia article](https://en.wikipedia.org/wiki/ISO_8601) about ISO-8601.
* To my **Rust**-based `file-8601` project [here](https://gitlab.com/brian_pond/file8601_rust)!
* (Deprecated) My original Python `file-8601` project [here](https://gitlab.com/brian_pond/file8601)

### Further Reading
* https://blog.ganssle.io/articles/2018/03/pytz-fastest-footgun.html
* https://blog.ganssle.io/articles/2018/02/aware-datetime-arithmetic.html
* https://github.com/regebro/tzlocal/issues/90#issuecomment-699714858
* https://github.com/dateutil/dateutil


### Attributions
Hourglass image on GitLab repository by The Oxygen Team, KDE; - KDE github;, LGPL, https://commons.wikimedia.org/w/index.php?curid=18609110
foundation-cancer-image-biomarker 1.0.1a8
Official repo for Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging [INSERT DOI]
License: MIT
Requires Python: <3.12,>=3.9


<div align="center">
This is the official repository for the paper
<br/><br/>
</div>

<div style="display: flex; flex-direction: column; align-items: center;">
    <img src="./docs/assets/Header.png" style="width: 100%;"/>
    <div style="display: flex; justify-content: space-between; width: 100%;">
        <img src="./docs/assets/Mhub_image.png" style="width: 49%;"/>
        <img src="./docs/assets/Mhub_image2.png" style="width: 50%;"/>
    </div>
    <a href="https://www.nature.com/articles/s42256-024-00807-9"><img src="./docs/assets/readpaper_logo.png" style="width: 100%;"></a>
</div>

<br/><br/>
<div align="center">

[![Build Status](https://github.com/AIM-Harvard/foundation-cancer-image-biomarker/actions/workflows/build.yml/badge.svg)](https://github.com/AIM-Harvard/foundation-cancer-image-biomarker/actions/workflows/build.yml)
[![Python Version](https://img.shields.io/pypi/pyversions/foundation-cancer-image-biomarker.svg)](https://pypi.org/project/foundation-cancer-image-biomarker/)
[![Dependencies Status](https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen.svg)](https://github.com/AIM-Harvard/foundation-cancer-image-biomarker/pulls?utf8=%E2%9C%93&q=is%3Apr%20author%3Aapp%2Fdependabot)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Security: bandit](https://img.shields.io/badge/security-bandit-green.svg)](https://github.com/PyCQA/bandit)
[![Pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/AIM-Harvard/foundation-cancer-image-biomarker/blob/master/.pre-commit-config.yaml)
[![Semantic Versions](https://img.shields.io/badge/%20%20%F0%9F%93%A6%F0%9F%9A%80-semantic--versions-e10079.svg)](https://github.com/AIM-Harvard/foundation-cancer-image-biomarker/releases)
[![License](https://img.shields.io/github/license/AIM-Harvard/foundation-cancer-image-biomarker)](https://github.com/AIM-Harvard/foundation-cancer-image-biomarker/blob/master/LICENSE)
[![Coverage](docs/assets/images/coverage.svg)](https://github.com/AIM-Harvard/foundation-cancer-image-biomarker/blob/master/docs/assets/images/coverage.svg)



---
**NOTE:**
 For detailed documentation check our [website](https://aim-harvard.github.io/foundation-cancer-image-biomarker/) 

---

</div>
ppi-toolkit 0.2.1
A Python library for retrieving, storing, and analyzing PPI data.
License: MIT
Requires Python: <4.0,>=3.12

# PPI Toolkit

A Python library for retrieving, storing, and analyzing **Producer Price Index (PPI)** data from the U.S. Bureau of Labor Statistics (BLS). The library supports:

- Downloading BLS PPI metadata and current commodity data  
- Storing the data in a local SQLite database  
- Searching PPI data series by fuzzy matching or direct lookups  
- Computing annualized percentage changes (1-month, 3-month, 6-month, and 12-month horizons)  

---

## Features

- **Automated Data Ingestion**  
  Fetch metadata and commodity data directly from the BLS text files, then store them in a local SQLite database.

- **Clean, Well-Structured Database**  
  - `metadata` table for series information (e.g. `series_id`, `group_code`, `series_title`, etc.)  
  - `commodities` table for monthly observations (`year`, `period`, `value`, etc.)

- **Fuzzy Text Search**  
  Quickly locate series by approximate matching on their titles.

- **Annualized Changes Calculation**  
  Compute monthly annualized percentage changes over 1-, 3-, 6-, and 12-month intervals.

- **Easy Plotting Hooks (Optional)**  
  Integration points for generating charts using `matplotlib`, `plotly`, etc.

---

## Quickstart

Below is a minimal example of how to install and use the library:

### 1. Install the Library

```bash
pip install ppi-toolkit
```

### 2. Set up the database
```
from ppi_toolkit.database import PPIDataBase

db = PPIDataBase()
db.setup_and_import_data()
```

3. Search for a series
```
from ppi_toolkit.search import PPISearcher

searcher = PPISearcher()  # Uses ~/ppi.db by default
results = searcher.search_titles("steel scrap")

for match in results:
    print(match)
    # Example output: {'series_id': 'WPS101211', 'series_title': 'Carbon steel scrap', 'score': 90}

```

4. Join and analyze data

```
from ppi_toolkit.joiner import PPIJoiner
from ppi_toolkit.analyzer import PPIAnalyzer

# 1. Join data from both tables
joiner = PPIJoiner()
df_joined = joiner.get_joined_data("WPS011101")
print(df_joined.head())

# 2. Compute annualized changes
analyzer = PPIAnalyzer()
df_trends = analyzer.compute_annualized_changes("WPS011101", 2018, 1, 2023, 12)
print(df_trends[["date", "value", "ann_1m", "ann_3m", "ann_6m", "ann_12m"]].tail())
```

5. Plot Data
```
from ppi_toolkit.plotting import plot_annualized_trends

df_trends = analyzer.compute_annualized_changes(
    series_id="WPS011101",
    start_year=2018, start_month=1,
    end_year=2023, end_month=6
)

# 2. Plot the results
plot_annualized_trends(
    df_trends,
    series_title="Iron and Steel Scrap",
    save_path="steel_scrap.png"
)
```

### License
This project is released under the MIT License. Feel free to use and modify it in personal or commercial projects, respecting the license terms.

### Acknowledgements 
- Data provided courtesy of the Bureau of Labor Statistics Producer Price Index.