semvec/pypi-packages huggingface dataset:
jelkasim 1.2.0
Simulation for running Jelka FMF patterns
Requires Python: >=3.8

# Jelka Simulation

Simulation for running Jelka FMF patterns.

## Note

If you want to contribute your own Jelka FMF patterns, you should check out the [Storži](https://github.com/Jelka-FMF/Storzi) repository for full instructions about developing and contributing patterns.

## Installation

```sh
pip install jelkasim
```

## Usage

```
usage: jelkasim.exe [-h] [--positions POSITIONS] run [run ...]

Run Jelka FMF simulation.

positional arguments:
  run                   How to run your program.

options:
  -h, --help            show this help message and exit
  --positions POSITIONS
                        File with LED positions. Leave empty for automatic detection or random.
```

Examples:

```sh
jelkasim moj_vzorec.py
jelkasim moj_vzorec.exe
jelkasim node moj_vzorec.js
jelkasim ocaml moj_vzorec.ml
```

For OCaml inside Docker, something like this should be used (probably):

```sh
jelkasim docker exec container-name "ocaml moj_vzorec.ml"
```

You should also figure out the container name using:

```sh
docker ps
```
notch 2026.0
The Python logging micropackage
Requires Python: >=3.10

# notch

🪓 The Python logging micropackage

## Purpose

You can use notch to quickly configure logging for your Python application. I designed notch
to encapsulate and standardize logging configuration for Python applications that are deployed
as container workloads. The default settings work in many simple cases, and you can further
customize logging behavior with environment variables.

## Usage

Import notch and call `configure()` to configure logging in your application entrypoint.

```python
import notch

notch.configure()
```

Call `configure()` as early as possible in your application startup, because notch in turn
calls `logging.basicConfig()` to configure the logging system.

Using the default settings, notch will configure the root logger to send logs to
`sys.stdout`, set the log level to `INFO`, and set the log format to
`'%(levelname)s [%(name)s] %(message)s'`.

## Configuration

You can change the behavior of notch by setting environment variables.

* `LOG_LEVEL`: change the log level for the root logger. This can be any level the Python
  logging module recognizes: `DEBUG`, `INFO` (the default), `WARNING`, `ERROR`, or `CRITICAL`.
* `LOG_FORMAT`: change the format for log output. Use any placeholders defined in the
  [Python logging documentation][a].
* `OTHER_LOG_LEVELS`: change the log level for loggers other than the root logger. See the
  explanation below.

[a]: https://docs.python.org/3/library/logging.html#logrecord-attributes

## Setting log levels for other modules

If you use third-party packages, you may want to set log levels for these packages
independently. For example, you may want to see `DEBUG` logs for your own code, but only
`WARNING` messages for other modules. You can do this by setting the `OTHER_LOG_LEVELS`
environment variable.

The value of this variable should be a module name and a log level separated by a colon.
You can set log levels for multiple modules by separating entries with a space.

```
OTHER_LOG_LEVELS=first_module:WARNING second_module:ERROR
```

For a concrete example, if your application depends on `boto3` but you only want to see errors
from `boto3`-related modules in your logs, configure like this:

```
OTHER_LOG_LEVELS=boto3:ERROR botocore:ERROR
```

You can specify submodules as well if you need to be more specific:

```
OTHER_LOG_LEVELS=apscheduler.scheduler:WARNING
```
vislearnlabpy 0.0.3.4
Visual Learning Lab utility files and pipelines
Requires Python: >=3.11

# vllpy

This is a package with common utility functions, files and pipelines for the Visual Learning Lab. This package uses python>=3.11.

It is recommended you create a conda environment to start using this package but this step is optional. To do so, run the commands below:

```
conda create -n vislearnlabpy python=3.12
conda activate vislearnlabpy
```

Then, activate the environment and simply install vislearnlabpy and CLIP by running the commands below in your terminal. 

```
pip install --upgrade vislearnlabpy
pip install git+https://github.com/openai/CLIP.git
```

You may also have to install [PyTorch](https://pytorch.org/) manually, ensuring that you have the right version but the right version may also be installed by default with CLIP. To install the right version of PyTorch on the Tversky server, run:
```
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
```

Here is as an example of how to generate a CSV file with embeddings from a list of images in a directory. You can also use this to generate npy files and doc files by changing the output type in the command below, and generate the embeddings from a CSV file instead by using `input_file` instead of `input_dir`
``` 
python embedding_generator.py --input_dir examples/input --output_path examples/output --output_type csv --overwrite
```

For more detailed examples, please look at the demo in the Jupyter notebooks within the `examples` folder.
python-rainwave-client 2026.0
Python client library for Rainwave
Requires Python: >=3.10

# Python Rainwave Client

This is a Python client library for [Rainwave][a]. Documentation is available at
[https://python-rainwave-client.subtlecoolness.com/][b].

[a]: https://rainwave.cc/api4/
[b]: https://python-rainwave-client.subtlecoolness.com/

## Requirements

This library requires no dependencies outside the Python standard library.

## Installation

Install with `pip`:

    pip install python-rainwave-client