semvec/pypi-packages huggingface dataset:
django_zenblog_ds 5.1.0
Demiansoft template(zenblog)
Requires Python: >=3.6

# django_zenblog_ds

demiansoft zenblog 템플릿
다른 템플릿과 차이는 블로그 전문 템플릿이라는 것

## 설치
1. pip를 이용해서 앱 설치
    ```bash
    pip install django_zenblog_ds
    ```
2. 프로젝트 settings.py에 앱 등록
   ```python
   import os
   
   INSTALLED_APPS = [
   "jazzmin", # 관리자 페이지 UI
   'django.contrib.admin',
   ...,
   'django.contrib.sitemaps',
   'shared_lib',
   'markdownx', # 블로그 마크다운에디터
   'hitcount', # 블로그 히트카운터
   'taggit', # 블로그 태그관리
   ...,
   'zenblog',
   ]
   
   # 추가 설정 사항들
   import os
   
   X_FRAME_OPTIONS = 'DENY' # 클릭재킹공격 방지 보안설정
   
   STATICFILES_DIRS = [
       os.path.join(BASE_DIR, '_static/'),
   ]
   
   MEDIA_URL = '/media/'
   MEDIA_ROOT = os.path.join(BASE_DIR, 'media/')
   
   STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles')
   
   from _data import shared_lib
   JAZZMIN_SETTINGS = shared_lib.JAZZMIN_SETTINGS
   MARKDOWNX_MARKDOWN_EXTENSIONS = shared_lib.MARKDOWNX_MARKDOWN_EXTENSIONS
   MARKDOWNX_MARKDOWN_EXTENSION_CONFIGS = shared_lib.MARKDOWNX_MARKDOWN_EXTENSION_CONFIGS
   MARKDOWNX_UPLOAD_MAX_SIZE = shared_lib.MARKDOWNX_UPLOAD_MAX_SIZE
   MARKDOWNX_UPLOAD_CONTENT_TYPES = shared_lib.MARKDOWNX_UPLOAD_CONTENT_TYPES
   MARKDOWNX_IMAGE_MAX_SIZE = shared_lib.MARKDOWNX_IMAGE_MAX_SIZE
   ```
3. 프로젝트 urls.py에 다음을 추가한다.
   ```python
   from django.urls import path, include
   from shared_lib import utils
   
   urlpatterns = [
    # robots.txt는 반드시 가장 먼저
    path('robots.txt', utils.robots),
    path('admin/', admin.site.urls),
    path('', include('zenblog.urls', namespace='zenblog')),
    path('shared_lib/', include('shared_lib.urls', namespace='shared_lib')),
    path('markdownx/', include('markdownx.urls')),
   ]
   
   # 개발 환경에서 미디어 파일 서빙
   from django.conf import settings
   from django.conf.urls.static import static
   
   if settings.DEBUG:
    urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
   ```
4. 프로젝트에 media/폴더를 생성하고 default_modal.bg.webp 와 default_thumbnail.jpg 를 넣어 모달 기본 배경으로 사용한다.
5. 모델 마이그레이션 생성(모달, 캘린더, 포트폴리오, 블로그 모델 설치)
    ```shell
    python manage.py makemigrations
    ```
6. 마이그레이션 적용
    ```shell
    python manage.py migrate
    ```
7. _data/shared_lib.py 와 _data/zenblog.py에 데이터 준비
django_medilab_dental 5.1.0
Demiansoft template(medilab)
Requires Python: >=3.6

# django_medilab_dental

demiansoft medilab 템플릿
다른 템플릿과 차이는 진료비 페이지가 있다는것

## 설치
1. pip를 이용해서 앱 설치
    ```bash
    pip install django_medilab_dental
    ```
2. 프로젝트 settings.py에 앱 등록
   ```python
   import os
   
   INSTALLED_APPS = [
   "jazzmin", # 관리자 페이지 UI
   'django.contrib.admin',
   ...,
   'django.contrib.sitemaps',
   'shared_lib',
   'markdownx', # 블로그 마크다운에디터
   'hitcount', # 블로그 히트카운터
   'taggit', # 블로그 태그관리
   ...,
   'medilab',
   ]
   
   # 추가 설정 사항들
   import os
   
   X_FRAME_OPTIONS = 'DENY' # 클릭재킹공격 방지 보안설정
   
   STATICFILES_DIRS = [
       os.path.join(BASE_DIR, '_static/'),
   ]
   
   MEDIA_URL = '/media/'
   MEDIA_ROOT = os.path.join(BASE_DIR, 'media/')
   
   STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles')
   
   from _data import shared_lib
   JAZZMIN_SETTINGS = shared_lib.JAZZMIN_SETTINGS
   MARKDOWNX_MARKDOWN_EXTENSIONS = shared_lib.MARKDOWNX_MARKDOWN_EXTENSIONS
   MARKDOWNX_MARKDOWN_EXTENSION_CONFIGS = shared_lib.MARKDOWNX_MARKDOWN_EXTENSION_CONFIGS
   MARKDOWNX_UPLOAD_MAX_SIZE = shared_lib.MARKDOWNX_UPLOAD_MAX_SIZE
   MARKDOWNX_UPLOAD_CONTENT_TYPES = shared_lib.MARKDOWNX_UPLOAD_CONTENT_TYPES
   MARKDOWNX_IMAGE_MAX_SIZE = shared_lib.MARKDOWNX_IMAGE_MAX_SIZE
   ```
3. 프로젝트 urls.py에 다음을 추가한다.
   ```python
   from django.urls import path, include
   from shared_lib import utils
   
   urlpatterns = [
    # robots.txt는 반드시 가장 먼저
    path('robots.txt', utils.robots),
    path('admin/', admin.site.urls), 
    path('', include('medilab.urls', namespace='medilab')),
    path('shared_lib/', include('shared_lib.urls', namespace='shared_lib')),
    path('markdownx/', include('markdownx.urls')),
   ]
   
   # 개발 환경에서 미디어 파일 서빙
   from django.conf import settings
   from django.conf.urls.static import static
   
   if settings.DEBUG:
    urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
   ```
4. 프로젝트에 media/폴더를 생성하고 default_modal.bg.webp를 넣어 모달 기본배경으로 사용한다.
5. 모델 마이그레이션 생성(모달, 캘린더, 포트폴리오, 블로그 모델 설치)
    ```shell
    python manage.py makemigrations
    ```
6. 마이그레이션 적용
    ```shell
    python manage.py migrate
    ```
7. _data/shared_lib.py 와 _data/medilab.py에 데이터 준비
django_herobiz_dental 5.1.0
Demiansoft template(herobiz)
Requires Python: >=3.6

# django_herobiz_dental

demiansoft herobiz 템플릿
다른 템플릿과 차이는 색살을 고를수 있다는 것
['', '-blue', '-green', '-orange', '-purple', '-red', '-pink']

## 설치
1. pip를 이용해서 앱 설치
    ```bash
    pip install django-herobiz-dental
    ```
2. 프로젝트 settings.py에 앱 등록
   ```python
   import os
   
   INSTALLED_APPS = [
   "jazzmin", # 관리자 페이지 UI
   'django.contrib.admin',
   ...,
   'django.contrib.sitemaps',
   'shared_lib',
   'markdownx', # 블로그 마크다운에디터
   'hitcount', # 블로그 히트카운터
   'taggit', # 블로그 태그관리
   ...,
   'herobiz',
   ]
   
   # 추가 설정 사항들
   import os
   
   X_FRAME_OPTIONS = 'DENY' # 클릭재킹공격 방지 보안설정
   
   STATICFILES_DIRS = [
       os.path.join(BASE_DIR, '_static/'),
   ]
   
   MEDIA_URL = '/media/'
   MEDIA_ROOT = os.path.join(BASE_DIR, 'media/')
   
   STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles')
   
   from _data import shared_lib
   JAZZMIN_SETTINGS = shared_lib.JAZZMIN_SETTINGS
   MARKDOWNX_MARKDOWN_EXTENSIONS = shared_lib.MARKDOWNX_MARKDOWN_EXTENSIONS
   MARKDOWNX_MARKDOWN_EXTENSION_CONFIGS = shared_lib.MARKDOWNX_MARKDOWN_EXTENSION_CONFIGS
   MARKDOWNX_UPLOAD_MAX_SIZE = shared_lib.MARKDOWNX_UPLOAD_MAX_SIZE
   MARKDOWNX_UPLOAD_CONTENT_TYPES = shared_lib.MARKDOWNX_UPLOAD_CONTENT_TYPES
   MARKDOWNX_IMAGE_MAX_SIZE = shared_lib.MARKDOWNX_IMAGE_MAX_SIZE
   ```
3. 프로젝트 urls.py에 다음을 추가한다.
   ```python
   from django.urls import path, include
   from shared_lib import utils
   
   urlpatterns = [
    # robots.txt는 반드시 가장 먼저
    path('robots.txt', utils.robots),
    path('admin/', admin.site.urls),
    path('', include('herobiz.urls', namespace='herobiz')),
    path('shared_lib/', include('shared_lib.urls', namespace='shared_lib')),
    path('markdownx/', include('markdownx.urls')),
   ]
   
   # 개발 환경에서 미디어 파일 서빙
   from django.conf import settings
   from django.conf.urls.static import static
   
   if settings.DEBUG:
    urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
   ```
4. 프로젝트에 media/폴더를 생성하고 default_modal.bg.webp를 넣어 모달 기본배경으로 사용한다.
5. 모델 마이그레이션 생성(모달, 캘린더, 포트폴리오, 블로그 모델 설치)
    ```shell
    python manage.py makemigrations
    ```
6. 마이그레이션 적용
    ```shell
    python manage.py migrate
    ```
7. _data/shared_lib.py 와 _data/herobiz.py에 데이터 준비
django_bocor_ds 5.1.0
Demiansoft template(bocor)
Requires Python: >=3.6

# django_bocor_dental

demiansoft bocor 템플릿


## 설치
1. pip를 이용해서 앱 설치
    ```bash
    pip install django_bocor_ds
    ```
2. 프로젝트 settings.py에 앱 등록
   ```python
   import os
   
   INSTALLED_APPS = [
   "jazzmin", # 관리자 페이지 UI
   'django.contrib.admin',
   ...,
   'django.contrib.sitemaps',
   'shared_lib',
   'markdownx', # 블로그 마크다운에디터
   'hitcount', # 블로그 히트카운터
   'taggit', # 블로그 태그관리
   ...,
   'bocor',
   ]
   
   # 추가 설정 사항들
   import os
   
   X_FRAME_OPTIONS = 'DENY' # 클릭재킹공격 방지 보안설정
   
   STATICFILES_DIRS = [
       os.path.join(BASE_DIR, '_static/'),
   ]
   
   MEDIA_URL = '/media/'
   MEDIA_ROOT = os.path.join(BASE_DIR, 'media/')
   
   STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles')
   
   from _data import shared_lib
   JAZZMIN_SETTINGS = shared_lib.JAZZMIN_SETTINGS
   MARKDOWNX_MARKDOWN_EXTENSIONS = shared_lib.MARKDOWNX_MARKDOWN_EXTENSIONS
   MARKDOWNX_MARKDOWN_EXTENSION_CONFIGS = shared_lib.MARKDOWNX_MARKDOWN_EXTENSION_CONFIGS
   MARKDOWNX_UPLOAD_MAX_SIZE = shared_lib.MARKDOWNX_UPLOAD_MAX_SIZE
   MARKDOWNX_UPLOAD_CONTENT_TYPES = shared_lib.MARKDOWNX_UPLOAD_CONTENT_TYPES
   MARKDOWNX_IMAGE_MAX_SIZE = shared_lib.MARKDOWNX_IMAGE_MAX_SIZE
   ```
3. 프로젝트 urls.py에 다음을 추가한다.
   ```python
   from django.urls import path, include
   from shared_lib import utils
   
   urlpatterns = [
    # robots.txt는 반드시 가장 먼저
    path('robots.txt', utils.robots),
    path('admin/', admin.site.urls),
    path('', include('bocor.urls', namespace='bocor')),
    path('shared_lib/', include('shared_lib.urls', namespace='shared_lib')),
    path('markdownx/', include('markdownx.urls')),
   ]
   
   # 개발 환경에서 미디어 파일 서빙
   from django.conf import settings
   from django.conf.urls.static import static
   
   if settings.DEBUG:
    urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
   ```
4. 프로젝트에 media/폴더를 생성하고 default_modal.bg.webp를 넣어 모달 기본배경으로 사용한다.
5. 모델 마이그레이션 생성(모달, 캘린더, 포트폴리오, 블로그 모델 설치)
    ```shell
    python manage.py makemigrations
    ```
6. 마이그레이션 적용
    ```shell
    python manage.py migrate
    ```
7. _data/shared_lib.py 와 _data/bocor.py에 데이터 준비
gurobipy-pandas 1.2.2
Pandas accessors for gurobipy interaction
Requires Python: >=3.8
Keywords: gurobipy, optimization, pandas

[![PyPI - Version](https://img.shields.io/pypi/v/gurobipy-pandas.svg)](https://pypi.org/project/gurobipy-pandas)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/gurobipy-pandas.svg)](https://pypi.org/project/gurobipy-pandas)
[![Tests](https://github.com/Gurobi/gurobipy-pandas/actions/workflows/main.yml/badge.svg?branch=main)](https://github.com/Gurobi/gurobipy-pandas/actions/workflows/main.yml?query=branch%3Amain++)
[![Docs](https://readthedocs.com/projects/gurobi-optimization-gurobipy-pandas/badge/?version=latest)](https://gurobipy-pandas.readthedocs.io/en/stable/)

# gurobipy-pandas: Convenience wrapper for building optimization models from pandas data

`gurobipy-pandas` is a convenient (optional) wrapper to connect pandas with gurobipy. It enables users to more easily and efficiently build mathematical optimization models from data stored in DataFrames and Series, and to read solutions back directly as pandas objects.

`gurobipy-pandas` is aimed at experienced pandas users who are familiar with methods to transform, group, and aggregate data stored in dataframes. It expects some familiarity with optimization modelling, but does not require deep experience with gurobipy.

## Features

`gurobipy-pandas` allows users to:

- create gurobipy variables tied to the index of a series or dataframe
- construct constraints row-wise using algebraic expressions
- read model solutions and constraint slacks natively as pandas series

## Installation

```console
pip install gurobipy-pandas
```

## Dependencies

- [gurobipy: Python modelling interface for the Gurobi Optimizer](https://pypi.org/project/gurobipy/)
- [pandas: powerful Python data analysis toolkit](https://pypi.org/project/pandas/)

## Documentation

Full documentation for `gurobipy-pandas` is hosted on [readthedocs](https://gurobipy-pandas.readthedocs.io/en/stable/).

## License

`gurobipy-pandas` is distributed under the terms of the [Apache License 2.0](https://spdx.org/licenses/Apache-2.0.html).

## Contact Us

For questions related to using gurobipy-pandas please use the [Gurobi Community Forum](https://support.gurobi.com/hc/en-us/community/topics/10373864542609-GitHub-Projects>).

For reporting bugs, issues and feature requests, specific to `gurobipy-pandas`, please [open an issue](https://github.com/Gurobi/gurobipy-pandas/issues).

If you encounter issues with Gurobi or `gurobipy` please contact [Gurobi Support](https://support.gurobi.com/hc/en-us).

## Contributors

- Simon Bowly (maintainer)
- Robert Luce (maintainer)
- [Irv Lustig](https://github.com/Dr-Irv) [Princeton Consultants](http://www.princetonoptimization.com)
- [Robert Randall](https://github.com/rrandall1471) [Princeton Consultants](http://www.princetonoptimization.com)

## Webinar

Slides for the 2023 webinars presenting this package can be found at [webinar/webinar.ipynb](https://github.com/Gurobi/gurobipy-pandas/blob/main/webinar/webinar.ipynb). The notebook will be presented as RISE slides, but can also be executed in Jupyter.
anndata-to-seurat 0.1.0
A package to convert Scanpy AnnData objects to Seurat objects.
Requires Python: >=3.6

# AnnData to Seurat Converter

A Python package to convert Scanpy AnnData objects to Seurat objects.

## Installation



### Conda installation 
Create a conda environment:

```bash
conda create -n scanpy_seurat python=3.8
conda activate scanpy_seurat

Install the package:

## Dependencies
anndata

scanpy

rpy2

numpy

pandas

Seurat (R package, installed via install.packages("Seurat") in R)

## Install the anndata_to_seurat package using pip

```bash 
pip install anndata_to_seurat




## Usage :

import scanpy as sc
from anndata_to_seurat import convert_anndata_to_seurat, generate_test_anndata

# Load your AnnData object or generate a test object
adata = generate_test_anndata()  # Generate a test AnnData object
# adata = sc.read("your_data.h5ad")  # Or load your own data

# Convert to Seurat
seurat_obj = convert_anndata_to_seurat(adata)

# Check if the conversion was successful
print(seurat_obj)
aa-data-handler 0.1.3
Handles data for the AA project
Requires Python: >=3.10

# AA Data Handler

## Description
AA Data Handler is a Python project designed to handle saving and loading data, considering various data types and file formats.

## Installation
To install the package, run:
```bash
pip install aa-data-handler
```

## Usage
### Saving Data
You can save a DataFrame, Series, dictionary, or Figure to a file using the `save` function:
```python
from pathlib import Path
import pandas as pd
from save import save

# Create a DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Save the DataFrame to a CSV file
save(df, 'data', 'csv', Path('data'))
```

#### Additional Examples
1. **Save a Series to a Pickle File:**
    ```python
    series = pd.Series([1, 2, 3, 4, 5])
    save(series, 'series_data', 'pickle', Path('data'))
    ```

2. **Save a Dictionary of DataFrames to an Excel File:**
    ```python
    data_dict = {
        'Sheet1': pd.DataFrame({'A': [1, 2], 'B': [3, 4]}),
        'Sheet2': pd.DataFrame({'C': [5, 6], 'D': [7, 8]})
    }
    save(data_dict, 'data_dict', 'xlsx', Path('data'))
    ```

3. **Save a Matplotlib Figure to a SVG File:**
    ```python
    import matplotlib.pyplot as plt

    fig, ax = plt.subplots()
    ax.plot([1, 2, 3], [4, 5, 6])
    save(fig, 'figure', 'svg', Path('data'))
    ```

### Loading Data
You can load a DataFrame, Series, or dictionary from a file using the `load` function:
```python
from load import load

# Load the latest file in the log
loaded_df = load(name='data')

# Print the loaded DataFrame
print(loaded_df)
```

#### Arguments for `load`
- `name` (str): The name of the file to load. If not provided, the function will search the log for the latest file.
- `file_format` (str): The format of the file to load. Supported formats are 'csv', 'xlsx', and 'pickle'.
- `file_path` (Path | str): The path to the file. If not provided, the function will search the log for the latest file.
- `log_id` (str): The unique file ID from the log file. If provided, the function will use this to locate the file.

#### Examples
1. **Load by Name:**
    ```python
    loaded_df = load(name='data')
    ```

2. **Load by Name and Format:**
    ```python
    loaded_df = load(name='data', file_format='csv')
    ```

3. **Load by Name, Format, and Path:**
    ```python
    loaded_df = load(name='data', file_format='csv', file_path='data')
    ```

4. **Load by Log ID:**
    ```python
    loaded_df = load(log_id='unique-log-id')
    ```

## Contributing
No need to contribute here, but thank you!

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
li-aws-helper 0.4.5
Package that helps manage aws credentials
Requires Python: <4.0,>=3.9

# li-aws-helper

[![CircleCI](https://dl.circleci.com/status-badge/img/gh/liniribeiro/li-aws-helper/tree/main.svg?style=svg)](https://dl.circleci.com/status-badge/redirect/gh/liniribeiro/li-aws-helper/tree/main)

li-aws-helper is a Python library designed to facilitate the management of AWS credentials and the refreshing of session tokens using MFA (Multi-Factor Authentication). This library provides a command-line interface (CLI) to configure AWS credentials and refresh session tokens easily.


## Installation
To install the li-aws-helper package, use the following command:

```
pip3 install li-aws-helper
```

## Configuration
Before using the library, you need to configure your AWS credentials. Run the following command to start the configuration process:

```bash
li config
```
he service will prompt you to provide the following information:  
- Region: The AWS region (default is us-east-1).
- Access Key: Your AWS access key.
- Secret Key: Your AWS secret key.
- MFA Identifier: The ARN of your MFA device.

All those answers you can find at AWS Console/ Security credentials.
> Note: The provided credentials will be stored locally in a file named credentials.json.
 
The library works, modifying your .aws/credentials for the mfa credentials, keeping your original credentials safe.
For rollback your original credentials, you can run the command:
```bash
li rollback
```
You shouldn't have to do this, but if you want to rollback the credentials, you can run the command above.

## Usage 
#### Refreshing Session Tokens
To refresh your AWS session tokens using MFA, use the following command:
```
li refresh --token <MFA_TOKEN_CODE>
```
Replace <MFA_TOKEN_CODE> with the actual MFA token code generated by your MFA device. 


You also can add a function to your .bashrc or .bash_profile to make the command shorter:
```
litoken(){
 li refresh --token $1
}
```
Then, you can refresh your session tokens by running:

```
litoken <MFA_TOKEN_CODE>
```

## Local Development
For local development, follow these steps:

1. Build the project:
```
poetry build
```
2. Install the project:
```
poetry install
```
3. Install twine for publishing:
```
python3 -m pip install --upgrade twine
```

## FAQ
- What is an ARN? An ARN (Amazon Resource Name) is a unique identifier for AWS resources.  
- Where can I find my AWS key and secret? You can find your AWS access key and secret key in the AWS Management Console under Security Credentials.


## Contributing
Contributions are welcome! Please open an issue or submit a pull request on GitHub.  

## Contact
For any questions or issues, please contact Alini Ribeiro at aliniribeiroo@gmail.com. 


This documentation provides an overview of the li-aws-helper library, including installation, configuration, usage, and development instructions.