semvec/pypi-packages huggingface dataset:
tendril-connector-grafana 0.2.4
Grafana Management Connector for Tendril
Requires Python: !=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7
Keywords: tendril



.. image:: https://img.shields.io/pypi/v/tendril-connector-grafana.svg?logo=pypi
    :target: https://pypi.org/project/tendril-connector-grafana

.. image:: https://img.shields.io/pypi/pyversions/tendril-connector-grafana.svg?logo=pypi
    :target: https://pypi.org/project/tendril-connector-grafana

.. image:: https://img.shields.io/travis/tendril-framework/tendril-connector-grafana.svg?logo=travis
    :target: https://travis-ci.org/tendril-framework/tendril-connector-grafana

.. image:: https://img.shields.io/coveralls/github/tendril-framework/tendril-connector-grafana.svg?logo=coveralls
    :target: https://coveralls.io/github/tendril-framework/tendril-connector-grafana

.. image:: https://img.shields.io/codacy/grade/363acc44e8c240dc9db79935860191b4?logo=codacy
    :target: https://www.codacy.com/app/chintal/tendril-connector-grafana

.. image:: https://img.shields.io/requires/github/tendril-framework/tendril-connector-grafana.svg
    :target: https://requires.io/github/tendril-framework/tendril-connector-grafana/requirements

.. image:: https://img.shields.io/pypi/l/tendril-connector-grafana.svg
    :target: https://www.gnu.org/licenses/agpl-3.0.en.html



.. inclusion-marker-do-not-remove

Introduction
------------

TODO Some brief introduction


Package Information
-------------------

The latest version of the documentation, including installation, usage, and
API/developer notes can be found at
`ReadTheDocs <https://tendril-connector-grafana.readthedocs.io/en/latest/index.html>`_.

The latest version of the sources can be found at
`GitHub <https://github.com/tendril-framework/tendril-connector-grafana>`_. Please use 
GitHub's features to report bugs, request features, or submit pull/merge requests.

The principle author for ``tendril-connector-grafana`` is Chintalagiri Shashank. The 
author can be contacted if necessary via the information on the
`author's github profile <https://github.com/chintal>`_ . See the AUTHORS file
for a full list of collaborators and/or contributing authors, if any.

``tendril-connector-grafana`` is distributed under the terms of the
`AGPLv3 license <https://www.gnu.org/licenses/agpl-3.0.en.html>`_ .
A copy of the text of the license is included along with the sources.
acmetric-plotting 1.3.2
A package to easily build ACMetric branded plots
Requires Python: >=3.7

<a href='https://acmetric.com/' target='_blank'><img src='https://i.postimg.cc/ZnscgcFb/Acmetric-Social-3.jpg' height="100" border='0' alt='Acmetric-Social-3'/></a> 
# Introducing ACMetric package!
### Current version: `1.3.2`

This package is created to help you use ACMetric's brand colors and build plots without hours of tuning. Enjoy!

## Installing on Google Colab 
Setting up in Google Colab is described [here](https://github.com/ACMetric/acmetric_package/blob/master/colab_setup.md).

## Importing
We recommend importing it along with `matplotlib` and `seaborn`.

```python3
%matplotlib inline # display plots in the notebook right away
%config InlineBackend.figure_format='retina' # high resolution
import matplotlib.pyplot as plt
import seaborn as sns
import acmetric_plotting as ac
```

And it is ready to go!

### You can find code examples here: [Jupyter](https://github.com/ACMetric/acmetric_package/blob/master/notebooks/acmetric_package_intro.ipynb) | [Google Colab](https://colab.research.google.com/drive/14eYxEthMcPohkTFC9CLhe-nzHbQDoEsu?usp=sharing)
***
## Some things you need to know

`ac.display_colors()` will show you a table with all the colors available and their names.

`ac.colors` module contains ACMetric colors, you can access them by writing `ac.colors.coral`, `ac.colors.sky_60`, etc.  

`ac.palette` is a `matplotlib` color palette. You can call it and choose a color you like by index, e.g. `ac.palette[3]`.

`ac.cmap` is a gradient colormap that can be used in `seaborn` heatmap and other plots.

Run `ac.params.layout_color('black')` to make axes and text black. Run `ac.params.layout_color('default')` to make them grey again.

Now 4 kinds of plots are available in the package: bar chart, pie chart, scatter plot and box plot. You can make them using `ac.bar`, `ac.pie`, `ac.scatter` and `ac.box`. All the possible parameters can be found in the docstring.

**Note:** it doesn't mean you can't build other kinds of plots. Just import `matplotlib` or `seaborn`, and all the plots you create will also be ACMetric branded!
pana 0.2.1
Pana is a CLI and module built to check if a package name or username is available from common package managers.
Requires Python: >=3.6

<h2 align="center">Pana - Pick a Name Already</h2>

<p align="center">
  Check if a package or username is available from some of the most popular package managers.
</p>


## Install

Pana is available from `pip`:

```bash
pip install pana
```

## Package Managers

| Package Manager                                              | Status               |
|--------------------------------------------------------------|----------------------|
| [PyPI](https://pypi.org/)                                    |  :white_check_mark:  |
| [npm](https://www.npmjs.com/)                                |  :white_check_mark:  |
| [NuGet](https://www.nuget.org/)                              |  :white_check_mark:  |
| [crates](https://crates.io/)                                 |  :white_check_mark:  |

## Examplea

### Module
```python
from pana import Pana

# Print both lists to stdout
print(Pana.check_pkg("pana"))
print(Pana.check_user("battleoverflow"))

# Example output
"""
[
  ['pypi', False],
  ['npm', False],
  ['nuget', True],
  ['crates/docs', False]
]
"""
```

### CLI

Search for a specific package

```bash
pana -p pana
```

Search for a username

```bash
pana -u battleoverflow
```
danila 1.0.0
This is the module for detecting and classifying text on rama pictures
Requires Python: >=3.6
Keywords: rama detect machine-learning computer-vision

# To install project made 
    git clone
    pip install -r requirements.txt
# All user methods are in 
    Danila class

# makes object and loads all neuro models
    danila = Danila()

# returns string - class of rama using CNN network
    def rama_classify(self, image_path)

# returns image_path in danilav1 root with drawn rectangle and text - rama and its class
    def rama_detect(self, image_path)

# returns image_path in danilav1 root with cut_rama 
    def rama_cut(self, image_path)

# returns image-path of cut rama with drawn text areas
    def text_detect_cut(self, image_path)

# returns image-path of image with drawn text areas
    def text_detect_cut(self, image_path)

# scripts illustrates methods using
    demo_1
    demo_2
    demo_3
    demo_4
    demo_5
    
# to start work you should 
    add directory yolo
    cd yolo/
    git clone https://github.com/ultralytics/yolov5.git

# you should paste directory models just in root
    https://disk.yandex.ru/client/disk/%D0%9A%D0%BE%D0%BC%D0%BF%D1%8C%D1%8E%D1%82%D0%B5%D1%80%20NUFDR0019114/%D0%A0%D0%90%D0%9C%D0%90
crepex-django-util 0.8.0
CrepeX django utilities
Requires Python: <4.0,>=3.8

# CrepeX django util

## 개발환경
- `poetry` 를 사용해서 개발
- 가상환경은 `poetry shell`
- 패키지 설치는 `poetry install`
- 최조 pre-commit 셋팅 `pre-commit install`

## 배포

### 테스트
- TestPypi 계정준비, API 토큰 준비

```shell
# test pypi repository 등록
poetry config repositories.testpypi https://test.pypi.org/legacy/
# poetry 에 토큰 등록
poetry config http-basic.testpypi __token__ pypi-your-api-token-here

# Build & Publish
poetry build
poetry publish -r testpypi

# Get package
pip install -i https://test.pypi.org/simple/ your-package-name
```

### Private 사용 (Now)

```shell
# Clone 후 설치
$ pip install git+https://[user-token]@github.com/[user-name]/[repo-name].git@[브랜치명|태그|해시]

# Clone 없이 설치된 버전 사용
$ pip install https://[user-token]@raw.githubusercontent.com/[user-name]/[repo-name]/[branch]/dist/[package_name]-[version].tar.gz

```
shinigami 0.2.1
Shinigami is an open source Python library allowing the user to generate and build Dockerfiles during runtime
Requires Python: >=3.6

<h1 align="center">
    <img src="https://raw.githubusercontent.com/battleoverflow/shinigami/main/assets/shinigami_logo.png" />
</h1>

<p align="center">
    <b>Shinigami was created to be simplistic and maintainable</b>
</p>

Shinigami is an open source Python library allowing the user to generate and build Dockerfiles during runtime.

## Usage

You can install Shinigami via pip:

```bash
pip install shinigami
```

### Example

```python
from shinigami import Shinigami

def create_file():
    Shinigami(lang_os="python", version="3.9", build=True, verbose=True, color=True).generate_dockerfile()

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

If you just want to generate the Dockerfile without building the container, you can do that too. Just remove the `build` boolean from the class and you should see a Dockerfile populate in your current directory within seconds.

## CLI

### Usage

Available commands:

```
-h,  --help      |  Help menu
-i,  --image     |  Docker image to generate
-v,  --version   |  Version of the Docker image
-b,  --build     |  Build the Dockerfile after generation
-c,  --color     |  Generate color in the output
-rm, --remove    |  Remove the Dockerfile in your current working directory
```

### Examples

This command will generate a Dockerfile for Ubuntu 22.04, but won't build the Docker image
```bash
shinigami -i "ubuntu" -v "22.04"
```

This command will build a Docker image running Python 3.8 and generate the Dockerfile
```bash
shinigami -i "python" -v "3.8" -b
```

Removes the Dockerfile in your current working directory
```bash
shinigami -rm
```