semvec/pypi-packages huggingface dataset:
Grigori 1.0.1
Watches a directory for file changes.
License: MIT

# Grigori

Grigori is a small Python 3 module that watches over your files and notifies you of changes.

## Usage

There are currently 2 ways you can use Grigori, with a for loop, or with callbacks.

**For loops**

For the complete example, see `/examples/basic.py`.

```python
w = Watcher(directory)

for changes in w.watch():
    for change in changes:
        print("file '%s' has been changed in the following way: '%s'" % (change["file"], change["type"]))
```

**Callbacks**

For the complete example, see `/examples/callbacks.py`.

```python
w = Watcher("./directory/to/watch")

# Bind the callbacks.
w.on(Change.ADDED, on_added)
w.on(Change.MODIFIED, on_modified)
w.on(Change.DELETED, on_deleted)

w.wait()  # Start watching the files.
```

## To Do

Allow for watching multiple top-level directories, which will be each watched in threads using a thread pool.

Add `inotify` support.

Add Handler classes that can be used instead of for loops or callbacks.
deep-plots 0.1.1
Visualize Your Deep Learning Training in Static Graphics
License: MIT

# Deep Plots [![Build Status](https://travis-ci.com/jfilter/deep-plots.svg?branch=master)](https://travis-ci.com/jfilter/deep-plots) [![PyPI](https://img.shields.io/pypi/v/deep-plots.svg)](https://pypi.org/project/deep-plots/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/deep-plots.svg)](https://pypi.org/project/deep-plots/)

Visualize Your Deep Learning Training in Static Graphics.

<div align="center">
  <img src="demo/loss.png" alt="Plot Loss">
</div>

**Why?** [Analyzing learning curves](https://www.coursera.org/lecture/machine-learning/learning-curves-Kont7) are a standard way to evaluate the learning performances of machine learning models. There exist [several tools](#Related) for creating live plots. This Python package focuses on producing beautiful static graphics only.

Currently, only plotting from [Keras CSV log file](https://keras.io/callbacks/#csvlogger) format is supported.

For creating the graphics, [plotnine](https://github.com/has2k1/plotnine) is used which is build upon [Matplotlib](https://matplotlib.org/).

## Installation

```bash
pip install deep_plots
```

Unfortunately, you may need to:

```bash
pip install numpy
```

before because a [depedency implicitly assumes numpy is installed](https://github.com/statsmodels/statsmodels/issues/3207).

## Usage

```python
# create a Keras callback to log your training
csv_logger = keras.callbacks.CSVLogger('log.csv')

# train your model
model.fit(X, y, ..., callbacks=[csv_logger, ...])

# after finishing training, plot the learning curves with Deep Plots
deep_plots.from_keras_log('log.csv', 'output_dir')
```

## Related

-   [TensorBoard](https://github.com/tensorflow/tensorboard): Live plots for TensorFlow, [Keras](https://keras.io/callbacks/#tensorboard).
-   [tensorboardX](https://github.com/lanpa/tensorboardX): Live plots for PyTorch, Chainer etc..
-   [Live Loss Plot](https://github.com/stared/livelossplot): Live plots in Jupyter Notebooks for Keras, PyTorch etc..

## Contributing

If you have a **question**, found a **bug** or want to propose a new **feature**, have a look at the [issues page](https://github.com/jfilter/deep-plots/issues).

**Pull requests** are especially welcomed when they fix bugs or improve the code quality.

## License

MIT.