semvec/pypi-packages huggingface dataset:
curved-surface-distance 0.0.1
this package can be used to calculate distance between two points in the curved surface
Requires Python: >=3.7

This package is used to calculate the distance between two points or a list of points in the curved surface using distance.
Using the HSV_distance the distance between two HSV colour code can be calculated.
    [H,S,V] where H refers to Hue ranges from o to 360, S represents saturation and V represents value that ranges from 0 to 100.
pyccmetrics 0.1.2
Python package for calculating code complexity metrics of the target source code
Requires Python: >=3.6

# Python Code Complexity Metrics

Python package for calculating code complexity metrics of the target source code.

## Description

With the help of this package, you can calculate the following code complexity metrics for your given source code as an input to it:

| Level   |      | Metric                                 | Variants         |
| ------- | ---- | -------------------------------------- | --------------- |
| methods | FOUT | Number of method calls (fan out)       | avg, max, total |
|         | MLOC | Method lines of code                   | avg, max, total |
|         | NBD  | Nested block depth                     | avg, max, total |
|         | PAR  | Number of parameters                   | avg, max, total |
|         | VG   | McCabe cyclomatic complexity           | avg, max, total |
|         |      |                                        |                 |
| classes | NOF  | Number of fields                       | avg, max, total |
|         | NOM  | Number of methods                      | avg, max, total |
|         | NSF  | Number of static fields                | avg, max, total |
|         | NSM  | Number of static methods               | avg, max, total |
|         |      |                                        |                 |
| files   | ACD  | Number of anonymous class declarations | value           |
|         | NOI  | Number of interfaces                   | value           |
|         | NOT  | Number of classes                      | value           |
|         | TLOC | Total lines of code                    | value           |

## Getting Started

### Support

Currently, the following source code languages is supported:

1. [x] Java
2. [ ] Python
3. [ ] JavaScript

### Dependencies

1. Python 3
2. Pip Package Manager

### Installing

```bash
pip install pyccmetrics
```

### Examples

- TBC

## Authors

**[Mohammad Mahdi Mohajer](https://github.com/mmohajer9)**

## License

This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details

## Acknowledgments & References

Inspired by the work of **[Thomas Zimmermann](https://ieeexplore.ieee.org/author/38563076700)**:

T. Zimmermann, R. Premraj and A. Zeller, "Predicting Defects for Eclipse," Third International Workshop on Predictor Models in Software Engineering (PROMISE'07: ICSE Workshops 2007), 2007, pp. 9-9, doi: 10.1109/PROMISE.2007.10. [Click for more info](https://ieeexplore.ieee.org/document/4273265).
Comparador-de-imagenes 0.0.1
Comparacion de imagenes.
Requires Python: >=3.8

# Imagen_processing


Description.

    Processing:

* Histrogram matching
* Structural similarity
* Resize image


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name


```bash
pip install imagen_processing
```

## Author

Esteban Gonzalez

## LIcense

[MLT](https://choosealicence.com\\licences/mit/)
gybe 0.2.2
A simple YAML transpilation tool for rendering kubernetes manifests

# Gybe

![PyPI Package](https://img.shields.io/pypi/v/gybe?color=%2334D058&label=PyPI%20Package)
![codecov.io](https://codecov.io/github/petermorrow/gybe/coverage.svg?branch=main)

Transpile Kubernetes manifests with your simple values.yaml files using python type-hints.
Gybe is a simple, declarative, and more pythonic alternative to [helm](https://helm.sh/)
that makes it easier to develop modest kubernetes deployments.

## Reqiurements

Python 3.7+

Gybe uses [pydantic](https://github.com/samuelcolvin/pydantic) for type-hint
validation and [click](https://github.com/pallets/click) for the CLI.

## Install

```
pip install gybe
```

## Example

Create a simple `values.yaml` file:

```yaml
image: python:3.9
command:
  - python
  - -m
  - http.server
```

Create a `chart.py` file:

```python
from typing import List

from gybe.favorites.kubernetes import Pod, PodSpec, Container
import gybe


def create_standard_container(image: str, command: List[str]):
    return Container(image=image, command=command, name='standard-server')


@gybe.transpiler
def two_pods(image: str, command: List[str], port: int=8080) -> gybe.Manifest:
    pod_spec = PodSpec(
        containers=[
            create_standard_container(image=image, command=command)
        ],
        ports=[dict(port=port)]
    )
    return [
        Pod(
            kind='Pod',
            apiVersion='v1',
            metadata=dict(name='pod-1'),
            spec=pod_spec,
        ),
        Pod(
            kind='Pod',
            apiVersion='v1',
            metadata=dict(name='pod-2'),
            spec=pod_spec,
        ),
    ]


if __name__ == '__main__':
    two_pods()
```

Now run your transpiler with your values file:

```bash
python chart.py values.yaml
```

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: pod-1
spec:
  containers:
  - command:
    - python
    - -m
    - http.server
    image: python:3.9
    name: standard-server
---
apiVersion: v1
kind: Pod
metadata:
  name: pod-2
spec:
  containers:
  - command:
    - python
    - -m
    - http.server
    image: python:3.9
    name: standard-server
```

If you're feeling lucky, you can pipe that into `kubectl`:

```bash
python chart.py values.yaml | kubectl apply -f -
```

```
pod/pod-1 created
pod/pod-2 created
```


```bash
kubectl get pods
```

```
NAME    READY   STATUS    RESTARTS   AGE
pod-1   1/1     Running   0          5s
pod-2   1/1     Running   0          5s
```

```bash
python chart.py values.yaml | kubectl delete -f -
```

```
pod "pod-1" deleted
pod "pod-2" deleted
```
izandescriptive 0.0.2
This library generates different graphics of numerical variables from a csv file.
Requires Python: >=3.7

![logo_azul](https://user-images.githubusercontent.com/64251090/199361725-4915b720-6e80-4e23-9342-e0ce48b58c7a.jpg)
-----------------

# Izandescriptive: powerful PyPi data analysis package 
[![PyPI Latest Release](https://img.shields.io/pypi/v/izandescriptive.svg)](https://pypi.org/project/izandescriptive/)
[![License](https://img.shields.io/pypi/l/izandescriptive.svg)](https://github.com/ceciliabilbao/izandescriptive/blob/main/LICENSE)

## What is it?

**Izandescriptive** is a PyPi library that provides fast and easy to undersand data
analysis. It aims to speed up the exploratory analysis process. It performs different graphs of the numerical variables in a single function called **graphic()**.

## Main Features

Here are just a few of the things that *izandescriptive* does well:

- Easy handling of reading csv.
- Selects numerical variables from the dataframe.
- Creates histograms, raincloud graphs and a heatmap graph.

### Class desc_analysis():

You can define the following class name desc_analysis

### Attributes

This class will have an attibute called *data*

- data : this attribute reads a csv and selects the numeric variables

### Methods

- graphic : The class desc_analysis has one method *graphic*. This method will take an argument wich is *data* defined in the previous attribute.

### Dependencies

- [pandas](https://pandas.pydata.org/)
- [turtle](https://docs.python.org/3/library/turtle.html)
- [ptitprince](https://github.com/pog87/PtitPrince)
- [seaborn](https://seaborn.pydata.org/)
- [matplotlib](https://matplotlib.org/)

## Using the library

### Installation

``` pip install izandescriptive ```

### Import

``` from izandescriptive import class_descriptives```

### Intance attributes and init the method

``` c1=desc_analysis('example.csv') ```

``` c1.graphic() ```

## Example

This would be the result of the desc_analysis class.

##### Histograms

![image](https://user-images.githubusercontent.com/64251072/197811387-d3f2e3f0-d28d-4332-ae28-fe35685f82be.png)

##### Raincloud

![image](https://user-images.githubusercontent.com/64251072/197811919-e4f5801a-07d0-4d27-af8d-ee427e882943.png)

##### Heatmap

![image](https://user-images.githubusercontent.com/64251072/197812131-81e48d49-ed32-4dc5-8328-e4980d4448fb.png)

## Where to get the library
The library is currently hosted on PyPi at:
https://pypi.org/project/izandescriptive/