semvec/pypi-packages huggingface dataset:
ConvDO 0.1.0
Convolutional Differential Operators with PyTorch

<h1 align="center">
  <img src="./docs/assets/logo/ConvDO.png" width="100"/>
  <br>ConvDO<br>
</h1>
<h4 align="center">Convolutional Differential Operators for Physics-based Deep Learning Study</h4>
<h6 align="center">Calculate the spatial derivative differentiablly!</h6>
<p align="center">
  [<a href="https://qiauil.github.io/ConvDO/">📖 Documentation & Examples</a>]
</p>

## Installation

* Install through pip: `pip install git+https://github.com/qiauil/ConvDO`
* Install locally: Download the repository and run `./install.sh` or `pip install .`

## Feature

Positive😀 and negative🙃 things are all features... 

* PyTorch-based and only supports 2D fields at the moment.
* Powered by convolutional neural network.
* Differentiable and GPU supported (why not? It's PyTorch based!).
* Second order for Dirichlet and Neumann boundary condition.
* Up to 8th order for periodic boundary condition.
* Obstacles inside of the domain is supported.

## Documentations

Check 👉 [here](https://qiauil.github.io/ConvDO/)

## Further Reading

Projects using `ConvDO`:

* [Diffusion-based-Flow-Prediction](https://github.com/tum-pbs/Diffusion-based-Flow-Prediction): Diffusion-based flow prediction (DBFP) with uncertainty for airfoils.
* To be updated... 

If you need to solve more complex PDEs using differentiable functions, please have a check on

* [PhiFlow](https://github.com/tum-pbs/PhiFlow): A differentiable PDE solving framework for machine learning
* [Exponax](https://github.com/Ceyron/exponax): Efficient Differentiable n-d PDE solvers in JAX.

For more research on physics based deep learning research, please visit the website of [our research group at TUM](https://ge.in.tum.de/publications/).
easycnn 0.1.0
EasyCNN: A library to create, train, and use custom CNN models with ease.
Requires Python: >=3.6

# EasyCNN

EasyCNN is a Python library that simplifies the creation, training, and usage of custom Convolutional Neural Networks (CNNs) for image processing. It allows users to input their own datasets and metadata files, select from a variety of popular CNN architectures, and visualize training results, all with minimal code.

## Features

- **Easy Model Selection**: Choose from over 20 popular CNN architectures.
- **Custom Training**: Train your models using your own datasets and metadata.
- **Visualization**: Visualize training and validation accuracy and loss.
- **Predictions**: Make predictions on new images using your trained model.
- **Automated Publishing**: Publish your models with GitHub Actions and PyPI.

## Installation

Install EasyCNN using pip:

```sh
pip install easycnn
```

# Usage
## Training a Model
To train a model using your own dataset and metadata:
```py
from easycnn import train_model, visualize_training

metadata_path = 'path/to/your/metadata.csv'
image_dir = 'path/to/your/images'
model_name = 'ResNet50'
epochs = 10
batch_size = 32

history, model = train_model(metadata_path, image_dir, model_name, epochs, batch_size)
visualize_training(history)
print("Model saved as 'trained_model.h5'")
```
## Making Predictions
To make predictions on new images using your trained model:
```py
from easycnn import predict_image

model_path = 'trained_model.h5'
image_path = 'path/to/your/image.jpg'

predicted_class = predict_image(model_path, image_path)
print(f"The predicted class is: {predicted_class}")
```
# Supported Model Architectures
EasyCNN supports 22 of the following CNN architectures:
- VGG16
- VGG19
- ResNet50
- ResNet101
- ResNet152
- InceptionV3
- InceptionResNetV2
- MobileNet
- MobileNetV2
- DenseNet121
- DenseNet169
- DenseNet201
- NASNetMobile
- NASNetLarge
- EfficientNetB0
- EfficientNetB1
- EfficientNetB2
- EfficientNetB3
- EfficientNetB4
- EfficientNetB5
- EfficientNetB6
- EfficientNetB7

# Contributing
Contributions are welcome! Please open an issue or submit a pull request on GitHub.

# License
This project is licensed under the MIT License. See the [LICENSE](https://github.com/yadavsidhant/easycnn?tab=MIT-1-ov-file) file for details.

# Contact
For any inquiries, please contact Sidhant Yadav at supersidhant10@gmail.com
minifind 0.0.3
os.walk wrapper with predicates

# minifind

minifind is os.walk wrapper with predicates

# Install

```shell
pip install minifind
```

# Use

```python
from minifind import find, size_kb
```

Find have two modes of operation:

1) trigger callback on each matched file / directory

```python
def callback(name, path):
    print(path)

find("path/to/dir").for_each(callback)
```

2) collect matched items

```python
paths = find("path/to/dir").collect()
```

# Examples

Find source files of size 5kb+:

```python
def not_moc(name, path):
    return not name.startswith('moc_')

paths = find("D:\\dev").ext(".cpp", ".h").filter(not_moc).size(size_kb(5)).collect()
```

Find git dirs, but dont go deeper that two directories in:

```python
paths = find("D:\\dev").maxdepth(2).name('.git').collect()
```

# Predicates and modifiers

```
.files() - match files

.dirs() - match dirs

.ext(*patterns) - match file extensions

.name(*patterns) - match name patterns

.size(low, high) - match file size

.mtime(low, high) - match modification time

.filter(predicate) - match with your predicate

.maxdepth(n) - don't go deeper than n levels in.

.first(n) - stop after n matched files
```

# See also

[https://github.com/mugiseyebrows/pyfindlib](https://github.com/mugiseyebrows/pyfindlib)
TimeToEvent 1.0.0
A simple Python package
Requires Python: >=3.6

# `basicpkg`

The `basicpkg` is a simple testing example
MIT License

Copyright (c) 2024 Or Shkuri

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.