semvec/pypi-packages huggingface dataset:
django-sass-finder 2.0.7
A Django finder that compiles Sass files
License: MIT
Requires Python: >=3.7,<4.0
Keywords: django,scss,staticfiles

# Django Sass Finder
A static files finder for Django that compiles Sass files

## Installation
### WARNING: MAKE SURE YOU HAVE NO SASS PACKAGES INSTALLED (other than libsass)!
Run `pip install django_sass_finder` to add this module to your virtualenv,
then add the finder to the list your static file finders as follows:

```python
STATICFILES_FINDERS = [
    # add the default Django finders as this setting will override the default
    'django.contrib.staticfiles.finders.FileSystemFinder',
    'django.contrib.staticfiles.finders.AppDirectoriesFinder',
    # our finder
    'django_sass_finder.finders.ScssFinder',
]
```
There is no need to add django_sass_finder into `settings.INSTALLED_APPS`.

The following additional (with examples) settings are used and required by this staticfiles finder:

```python
BASE_DIR = ...

SCSS_ROOT = BASE_DIR / 'scss'   # where the .scss files are sourced
SCSS_COMPILE = [                # a list of filename pattern to search for within SCSS_ROOT
    'site.scss',                # default is **/*,css (all scss source files in and below SCSS_ROOT)                                                                                                                                                                                                                                                                                            
    'admin/admin.scss',
]
SCSS_INCLUDE_PATHS = [          # optional: scss compiler include paths (default = empty)
    BASE_DIR / 'node_modules'
]
CSS_STYLE = 'compressed'            # optional: output format 'nested', 'expanded','compact','compressed'
CSS_MAP = True                      # optional: generate a source map
CSS_COMPILE_DIR = BASE_DIR / 'static' / 'css'   # The target directories for the compiled .css
STATICFILES_ROOT = [                            # this should be at or above the CSS_COMPILE_DIR
    BASE_DIR / 'static'                         # but targetting {app}/static should also work
]
```

`BASE_DIR` and variants above are `pathlib.Path` objects, but path strings can also be used.


## Usage
This module dynamically compiles to target .css files, and recompiles them on demand whenever
they are updated.

The `collectstatic` management command compiles these, and lets the `FilesystemFinder` transfer
them to STATIC_ROOT. The development server is perfectly able to serve these from
STATICFILES_ROOT without the need to `collectstatic`.

## License
This package is licensed under the MIT license.
django-environment-vars 0.1056b0
A Django environment variables manager.
License: MIT
Requires Python: >=3.6


# how to use

Installl
```

pip install django-environment-vars

```
1. Add config files


```
  INSTALLED_APPS = [...
          'django_contrib.environment_vars ',
      ]


```

2. Include the app URLconf in your project urls.py like this::


```
path('env/', include('django_contrib.environment_vars.urls'))

```


3. Start the development server and visit http://127.0.0.1:8000/env/


4. Or Visit demo site https://bygregonline.github.io/react_env_sample/
pmatcher 0.1.14
Monadic election precinct matcher for gerrymandering data collection and research at MGGG
License: MIT
Requires Python: >=3.7,<4.0

## (monadic) Precinct Matcher
Matching election data to shapefiles is hard.
It is usually context-dependent and implemented on a project-by-project basis.
It also sometimes involves some manual labor.
This attempts to make life easier for everyone who has to deal with precinct matching.

## Install

``` bash
pip install pmatcher
```
## Benchmarks (on real data)
VEST releases its precincts with VTD codes and county FIPS codes.
To validate this approach, I ran the matcher on known, good data.

Results (in % accuracy):
``` 
Exact match 0.9444831591173054
Insensitive match 0.9444831591173054
Insensitive normalized match 0.9932636469221835
Aggressive insensitive normalized match 0.9983739837398374
```

## Implemented Methods
- `matcher.default()`
Applies exact, insensitive, normalized, and weighted_manual in that order.
All batteries included!

- `matcher.exact()`
Matches exact strings.

- `matcher.insensitive()`
Matches strings (case-insensitive).

- `matcher.insensitive_normalized()`
Matches strings with special characters removed (e.g.`()`, `#`, `-`).

- `matcher.weighted_manual()`
Uses a weighted levenshtein algorithm.
First looks for token-distance, followed by token word distance for tiebreaking.

### Saving and loading progress
- `matcher.save_progress("progress.json")`
Saves progress/mapping to a json file.

- `matcher.load_progress("progress.json")`
Loads progress/mapping from a json file.

## Example usage

``` python
from pmatcher import PrecinctMatcher
matcher = PrecinctMatcher(list_1, list_2)
mapping = matcher.default()
```

``` python
from pmatcher import PrecinctMatcher
matcher = PrecinctMatcher(list_1, list_2)
matcher.exact()
matcher.insensitive()
matcher.insensitive_normalized()
matcher.insensitive_normalized(aggressive=True)
mapping = matcher.weighted_manual()
```