semvec/pypi-packages huggingface dataset:
bspcq 0.1.58
A `bspc` analyzer & a utility for bspwm
Requires Python: >=3.10
Keywords: bspwm

# bspcq, q for query

## A `bspc` analyzer (utility for [bspwm](https://github.com/baskerville/bspwm))

This is a small program that prints a user friendly, visual representation, of
your current `bsp` tree.

The aim is to assist in using `bspwm` so new users have an easier time getting
into tiling window managers, and have some toys to play with.

It's essentially the same as
running:

```sh
bspc query -M -m <monitor-name> | jq
```

Except there are fewer parameters, a less complex syntax to remember and an
intuitive visual.

## Installation

I'd recommend just copying the script from the root of this directory, `bspcq`,
and changing the shebang.

```sh
git clone https://github.com/aidenlangley/bspcq
mv bspcq/bspcq ~/bin/bspcq # or ~/.local/bin/bspcq
chmod +x ~/bin/bspcq
```

Then edit the shebang - my shebang:

```sh
#! /usr/bin/env python3.10
```

Your shebang might need to be be:

```sh
#! /usr/bin/env python3
```

### Dependencies

In addition, some Python modules are required, as is `xtitle`.

#### [`rich`](https://pypi.org/project/rich/)

`rich` is responsible for the nicely formatted tree.

```
python -m pip install rich
```

#### [`xtitle`](https://github.com/baskerville/xtitle)

`xtitle` assists in getting the title name of a node/window in X.

Requires C/C++ development tools, varies by distro.

```
git clone https://github.com/baskerville/xtitle
cd xtitle
make && make install
```

I've actually included it here, under contrib, so you can just copy it to `~/bin`.

### Via `pipx`

https://pypi.org/project/bspcq/

```sh
python -m pipx install bspcq
```

## Upgrade

```sh
python -m pipx upgrade bspcq
```

## Usage

```sh
# Prints the full tree, including output from all monitors.
bspcq
```

```sh
# Provides a simpler view, perfect for finding window class names.
bspcq -s
```

```sh
# Grabs info on all nodes that belong to the specified domain.
bspcq -M/D/N

# For a simplified view.
bspcq -M/D/N -s
```

```sh
# Passes through a query to `bspc query` and prints our tree, valid identifiers
# are names, ID's or the address output by `bspc -M/D/N`.
bspcq -m/d/n <monitor/desktop/node-identifier>
```

### Preview

![2022-01-25_02-17_1](https://user-images.githubusercontent.com/684721/150789813-da7d0b56-1762-4bf7-af6b-7d031f779030.png)

![2022-01-25_02-17](https://user-images.githubusercontent.com/684721/150789957-06765616-661b-4486-b69a-a7b570e204e1.png)

### Plans

My goals currently are to:

- Align `bspcq` as closely as possible with `bspc query`. I would like for
  `bspcq` to behave almost identically to `bspc query` - it just adds a visual
  element.
- Remain as lean as possible. A nice-to-have feature would be 0 reliance on
  external libraries, so I'd like to remove `rich` and implement a way to display
  the `tree` myself.

This was just an afternoon project that's turning into a week long project since
it's quite a lot of fun!
metaheuristic-clustering 0.0.2
sklearn and pyclustering style implementations of SFLA and ABC
Requires Python: >=3.7

# Metaheuristic Clustering

[![version status](https://img.shields.io/pypi/v/metaheuristic_clustering.svg?style=flat)](https://pypi.org/project/metaheuristic-clustering)

As the name suggests, this is a repository for metaheuristic clustering algorithms, implemented in Python 3, that I could not find implemented elsewhere.

Implementations are designed to work with or without the sklearn implementation style.

Currently the algorithms implemented are:
- Artifical Bee Colony (ABC)
    - Karaboga and C. Ozturk, "A novel clustering approach: Artificial Bee Colony (ABC) algorithm," Applied soft computing
- Shuffled Frog Leaping Algorithm (SFLA)
    - Amiri, B., Fathian, M., & Maroosi, A. (2009). Application of shuffled frog-leaping algorithm on clustering. The International Journal of Advanced Manufacturing Technology, 45(1), 199-209.
    
## Dependencies
[Numpy](https://numpy.org/)

[PyClustering](https://github.com/annoviko/pyclustering/) 

[scikit-learn](https://scikit-learn.org/stable/) - only needed for interop with scikit-learn

## Example

### Sklearn/Object style

```python
data = X  # your data

# SFLA Clustering
from metaheuristic_clustering.sfla import SFLAClustering

sfla_model = SFLAClustering()
sfla_labels = sfla_model.fit_predict(data)

# ABC Clustering
from metaheuristic_clustering.abc import ABCClustering

abc_model = ABCClustering()
abc_labels = abc_model.fit_predict(data)
```

### Function style

```python
import metaheuristic_clustering.util as util

data = X  # your data

# SFLA Clustering
import metaheuristic_clustering.sfla as sfla

best_frog = sfla.sfla(data)
sfla_labels = util.get_labels(data, best_frog)

# ABC Clustering
import metaheuristic_clustering.abc as abc

best_bee = abc.abc(data)
abc_labels = util.get_labels(data, best_bee)
```

### Sample Results

#### ABC
![Graphs of ABC Results](https://github.com/ElizabethForest/metaheuristic_clustering/blob/master/ABC_results.png)

#### SFLA
![Graphs of SLFA Results](https://github.com/ElizabethForest/metaheuristic_clustering/blob/master/SFLA_results.png)
arcus-azureml 1.1.2.2.2022050301rc0
A Python library to improve MLOps methodology on Azure Machine Learning
Requires Python: >=3.6

# Arcus for Azure Machine Learning

This package for Python allows users to better collaborate around Azure MLOps and abstracts a lot of the complexities when running experiments and trainings locally or in the cloud.

Support for

- Remote tracking of experiments
- Distributed training