semvec/pypi-packages huggingface dataset:
tear 0.0.2
TExt Analytics for Reconnaissance.
Keywords: dynamics, integration, seismic, earthquake-engineering

This version of code is refactored by Vedant Mathur

# TAR Software Package Repository

Code combining preprocessing, data collection, training and inference to generate automated disaster reports.

## Key Files 
* `tar_main.py` - File that consolidates relevant functions to produce a report 
* date2template* - Files that do different collectiong/processing of USGIS data to be added to the briefings 
* `classifiers.py` - Calls classifiers (regression, SVN, GAN, CNN) and runs a majority vote to determine the final classification for sentences according to 4 categories (buildings, infrastructure, resilience, other) 
* `resilience_curve.py` - Generates resilience curves, and calculates t0 and t1 (to calculate recovery time for disaster) 
* `config.ini` - Set of parameters to control briefing generation
* data - Folder containing log of earthquakes, tweets and news articles

## Usage
**Generating a report**

To generate a report, run 

``` 
python -m tear
```

This will iterate through earthquakes listed in the earthquake log and output a report to the "reports" directory. 

### Generating a resilience curve

To do this, call the `generateResilience` function in `resilience_curve.py`. It takes the following parameters - 

* `ruptureTime` - Reference time to when the earthquake happened (e.g. 2021-02-24 02:05:59)
* `twitterFile` - CSV with tweets for earthquake
* `keywords` - keywords to filter tweets by


For example:

```python
generateResilience("2021-02-24 02:05:59", "data/tweets/ArgentinaTweets.csv", ["electricity", "lights"])
```
recommend-games 0.0.1
A small recommendation engine for portfolio
Requires Python: >=3.12

PYTHON RECOMMENDATION ENGINE

The next portfolio project for CS Certification on Codecademy.

This one is a recommendation engine, intended to give suggestions to a user based on inputs.

I have chosen to make a video game recommendation engine that will return a list of games based on genre
and ranked by their MetaCritic ratings.

This will be an interactive terminal app, similar to the previous, but will use graphs, vertices, and BFS.

Currently I have the vertex/graph/bfs methods defined, with some examples to test the features. 

I decided to separate the portfolio projects rather than dump them all into one repo to make it easier to parse files.

I now have a base line for all files except the print statements. Once I get a template for those I will flesh everything out.

I will likely incorporate logging as I did in the previous project as well. 

12-13-24

At this point the app is feature complete. It is just a small proof of concept that may contribute toward a larger
personal portfolio project I am still conceptualizing.

I will be putting it to PyPI as well.