semvec/pypi-packages huggingface dataset:
convnets 0.2.9
Convolutional Neural Networks and utilities for Computer Vision
Requires Python: >=3.8,<4.0

# convnets

🚧 Under construction

Convolutional Neural Networks and utilities for Computer Vision.

- [Learn](learn) about convnets.
- Learn about popular [models](models).

## Models API

`convnets` offers implementations for the following models:

- [LeNet](http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf)
- [AlexNet](https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf)
- [VGG](https://arxiv.org/abs/1409.1556)
- [ResNet](https://arxiv.org/abs/1512.03385)

To instantiate a model you need to import the corresponding class and pass a valid `configuration` object to the constructor:

```python
from convnets.models import ResNet

r18_config = {
	'l': [
		{'r': 2, 'f': 64},
		{'r': 2, 'f': 128},
		{'r': 2, 'f': 256},
		{'r': 2, 'f': 512}
	], 
	'b': False
}

model = ResNet(r18_config)
```

Or you can use one of the predefined configurations, or variants:

```python
from convnets.models import ResNet, ResNetConfig

model = ResNet(ResNetConfig.r18)
```

You can find the implementation of each model and configuration examples in the [`convnets/models`](convnets/models) directory.

## Training API

If you want to train a model in your notebooks, you can use our [fit](convents/train/fit.py) function:

```python
form convnets.train import fit 

hist = fit(model, dataloader, optimizer, criterion, metrics, max_epochs)
```

You can use any Pytorch model. You will need to define the Pytorch dataloader, optimizer and criterion. For the metrics, the function expects a dict with the name of the metric as key and the metric function as value. The metric function must receive the model output and the target and return a scalar value. You can find some examples in [`convnets/metrics`](convnets/metrics.py). The `max_epochs` parameter is the maximum number of epochs to train the model. The function will return a dict with the training history. 

Additionally, we offer a [training script](train/train.py) that you can execute from the command line.

```bash
python scripts/train.py <path_to_config_file>
```

You will have to pass the path to a yaml file with the configuration for your training, including the model, optimizer, criterion, metrics, dataloader, etc. You can find some examples in the [`configs`](scripts/configs) directory (which are `timm` and `pytorch-lightning` compatible).

We also offer Pytorch Lightning interoperability.
recentearthquake 0.3
This package will get recent earthquake from BMKG (Meteorological, Climatological, and Geophysical Agency)
Requires Python: >=3.6

# Recent Indonesia Earthquake
This package will get recent earthquake from BMKG (Meteorological, Climatological, and Geophysical Agency)

## HOW IT WORKS?
This package will scrape from [BMKG](https://www.bmkg.go.id.com) to get recent earthquake happened in Indonesia

This package will use BeautifulSoup4 and Request, to then produce output in the form of JSON that is ready to be used in web or mobile applications

## HOW TO USE
    import recentearthquake

    if __name__ == '__main__':
        result = recentearthquake.ekstraksi_data()
        recentearthquake.tampilkan_data(result)

## Author
Arif Setyadi
streambox 0.0.2
A package for generic data science utilities
Requires Python: >=3.8

# Streambox - Data Science Toolkit

Streamkit is a toolkit with a number of functions and decorators useful for data scientist and developers in their projects.

## Installation

You can install this package either by cloning/forking this repo and install it locally,
or by fetching it from PyPIP repo.

### Local Installation
If you want to hot reload new changes, from repo root run: 

```bash
pip install -e . --upgrade --upgrade-strategy only-if-needed
```

### PyPIP Installation

```bash
pip install streambox
```

## Changelog
- **0.1.0** (23 March 2023)

    This is the genesis version released.
ucsd-ece-courses 0.0.1
UCSD ECE Department's course catalog
Requires Python: >=3.10
Keywords: python,ucsd,web-scraping

# ucsd-ece-courses

A neat package to scrape, organize, and filter for UCSD ECE Department's course catalog. Under construction!

Developed by Kendrick Nguyen, who is currently experimenting with packaging.

## Installation

```python
pip3 install ucsd_ece_courses
```

## Examples of How To Use

Get course descriptions

```python
from ucsd_ece_courses import ECECatalog

catalog = ECECatalog()

# Get ECE 5 courage descriptions, try also 'ece 5', 'ece_5', '5', etc.
course = 'ece_5'
print(catalog.course_title(course))
print(catalog.course_credit(course))
print(catalog.course_summary(course))
print(catalog.course_prerequisite(course))
```

Get all course numbers and titles

```python
from ucsd_ece_courses import ECECatalog

catalog = ECECatalog()

# Get all course numbers and titles
print(catalog.course_numbers)
print(catalog.course_titles)
```