semvec/pypi-packages huggingface dataset:
ChartSmith 0.1.2
A package for creating well-formatted, well-labeled visualizations
Requires Python: >=3.6

# ChartSmith

ChartSmith is a Python package built on top of Matplotlib and Seaborn that creates well-formatted, well-labeled visualizations.

## Installation

```sh
pip install ChartSmith
```

## Usage

Here's how you can use the ChartSmith package:

```python
from ChartSmith import ChartSmith

# Create an instance of the ChartSmith class
viz = ChartSmith()

# Line plot
viz.line_plot(x=[1, 2, 3, 4], y=[10, 20, 25, 30], title='Line Plot', xlabel='X Axis', ylabel='Y Axis')

# Bar plot
viz.bar_plot(x=['A', 'B', 'C', 'D'], y=[10, 20, 25, 30], title='Bar Plot', xlabel='Categories', ylabel='Values')

# Scatter plot
viz.scatter_plot(x=[1, 2, 3, 4], y=[10, 20, 25, 30], title='Scatter Plot', xlabel='X Axis', ylabel='Y Axis')
```

## License
This project is licensed under the MIT License - see the LICENSE.txt file for details.
stable-solvers 0.1
Library of solvers to train neural networks without entering the edge of stability

## Stable Solvers <img align="right" src="docs/source/_static/logo.png" width=128 height=128>

This library provides solvers for training neural networks without entering the edge of stability. The edge of stability phenomenon was discovered in [Cohen et al. 2021](https://arxiv.org/abs/2103.00065). This repo is based on the approach used in [Lowell and Kastner 2024](https://arxiv.org/abs/2406.00127), but it is a complete reimplementation incorporating new techniques that improve runtime efficiency and support more recent versions of PyTorch. The purpose of this library is to support scientific research investigating the true gradient flow of on the loss landscape. These solvers are too computationally expensive to be used in practice. You can find documentation [here](https://www.theorem-engine.org/stable_solvers/). You can install this library by downloading this repo, or by running:

```
pip install stable-solvers
```

Training neural networks without entering the edge of stability requires departing somewhat from the conventional syntax in PyTorch, because the solver needs to have direct access to the loss function so it can calculate eigenvectors and eigenvalues. The recommended syntax in this repo is:

```
import stable_solvers as solvers

net = ...
dataset = ...
criterion = ...

loss_func = solvers.LossFunction(
    dataset=dataset,
    criterion=criterion,
    net=net,
    num_workers=1,
    batch_size=32
)
params = loss_func.initialize_parameters()
solver = solvers.ExponentialEulerSolver(
    params=params,
    loss=loss_func,
    max_step_size=0.01,
    stiff_dim=...  # Should be equal to the dimension of the network outputs
)
loss = float('inf')

while loss > 0.1:
    loss = solver.step().loss
```

A full example can be found in the [example notebook](Exponential-Euler-Solver-Example.ipynb).
clientmonitor 0.1.0
Collect client data and display on monitor
Requires Python: >=3.8

# Multi process monitor

This is a small helper library intended for monitoring multiple processes outputting the same data set, visualizing it in a ncurses table.

> Example: Multiple scrapers processing different URLs logging their progress to one central monitoring entity.

## Installation
```bash
pip install clientmonitor
```

## Usage
This library consists of a server and a client. The Server accepts connections from clients and prints data received from the clients.

The clients expect the server to be running before connecting.

### Running the server
Port and secret are mandatory, host is optional and only relevant if server and client are not being run on the same machine.

```python
from clientmonitor.Server import Server

server = Server(6666, "secret", host="localhost")
server.set_columns([
  {
    "key": "key1",
    "label": "Label for Key 1"
  },
  {
    "key": "key2",
    "label": "Label for Key 2"
  }
])
server.start()

# CTRL-C to stop
```

The columns being set are the columns that are expected to be received as data from the clients and are what the server will print.

### Running the client
```python
import time
from clientmonitor.Client import Client

client = Client("client-1", 6666, "secret")
client.connect()

for i in range(0, 5):
  client.send({
    "key1": 100 + i,
    "key2": 200 + i,
  })

  time.sleep(5)

client.close()
```

## Development
Pull request are welcome, pleas address them against the develop branch.

### Publish
```bash
python -m build
python3 -m twine upload --repository testpypi dist/*
```