semvec/pypi-packages huggingface dataset:
markerpath 0.1.17
All imports are relative to the presence of the marker file
Requires Python: >=3.6

Usage:

1. Create a file with extension .marker in your base directory.  
2. Contents of this file
    an empty file
    OR
    lines of path relative to the base directory marked by the marker file. These paths will be appended to sys.path
    OR
    add_all_python_paths

3. In your python file with main anywhere under the base directory.
    import markerpath

    3.1 This will create a environment variable 
        MARKERPATH=<the base directory>

    3.2 Access this as such
        import os
        marker_home=os.environ["MARKERPATH"]
4. Provides a global dictionary called 
    markerpath_global 
    key "logger" that holds the python logger object.

5. Provides a inject_logger_member that will inject the self.logger ( or any name you chose ). This member can be used in the __init__

6. The user can initialize the global markerpath_global with markerpath_global_initialize

7. The config value is parsed if passed ar an argument --config  or in defaults  like
    markerpath_confg.xml
    config/markerpath_config.xml
adpro-video-ferramentas-Adbreyk-24-ad 1.0
Este pacote irá fornecer ferramentas de processamento de vídeo
Keywords: Camera, Vídeo, processamento

# Adprovideo Ferramentas 

DescricÃ£o de como seu pacote funcioana...

* Passos para instalaÃ§Ã£o 
* outras info. relevantes(autores.empresa,contato)
atypical 0.4.0
Type handling and normalization for non-standard types like email, phone numbers, money, dates, etc. serializable and deserializable as JSON, JSON Schema, and Pydantic.
Requires Python: >=3.9
Keywords: atypical, utils

# atypical
Custom types for things like phone numbers, emails, etc. with normalization, Pydantic handling and JSON Schema serialization

## Examples

```python
from atypical.email import Email
from atypical.money import Money
from atypical.phone import PhoneNumber
from atypical.url import NormalizedURL
from sartorial.schema import Schema


class MyModel(Schema):
    email: Email
    phone_number: PhoneNumber
    amount: Money
    website: NormalizedURL

import json
print(MyModel.model_json_schema())  # or MyModel.to_schema_dict()

# Output
'''
{
    "additionalProperties": true,
    "properties": {
        "email": {
            "format": "email",
            "type": "string"
        },
        "phone_number": {
            "format": "phone",
            "type": "string"
        },
        "amount": {
            "format": "money",
            "type": "string"
        },
        "website": {
            "format": "normalized-url",
            "type": "string"
        }
    },
    "required": [
        "email",
        "phone_number",
        "amount",
        "website"
    ],
    "title": "MyModel",
    "type": "object"
}
 '''

m = MyModel(email="foo.bar+baz@gmail",
            phone_number="1 (212) 555-6789",
            amount="$100",
            website="example.com")

print(m.model_dump_json(indent=4))  # or m.to_json()

# Output
'''
{
    "email": "foobar@gmail.com",
    "phone_number": "+12125556789",
    "amount": "$100.00",
    "website": "https://example.com/"
}
'''
```
nycschools 0.1.18.1
Tools to work with open data about New York City public schools
Requires Python: >=3.8
Keywords: opendata, schools, newyorkcity, maps

New York Schools Open Data Project
==================================

The mission of the NYC Schools Open Data Project is to make open data regarding public schools in New York available to everyone interested in improving our schools, regardless of their technical background or familiarity with data analysis. We believe that fighting for educational equity requires us to broaden the audience of who can use and analyze data. Accordingly, we want to make educational data more accessible, train people to use it, and demystify and remove the barriers to access.

Our goal is to build platform where any motivated individual or groups can evaluate available school data, interpret it to help understand issues they care about, and employ data as part of larger arguments in our conversations about schools in New York.

**[Find technical documentation and examples here](https://adelphi-ed-tech.github.io/nycschools/)**
drs-id-mongo-publisher 0.1.0
A utility for populating Mongo with DRS Object records
Requires Python: >=3.12

# DRS ID Mongo Publisher

<a href="https://github.huit.harvard.edu/LTS/drs-id-mongo-publisher/actions/workflows/pytest.yml"><img src="https://github.huit.harvard.edu/LTS/drs-id-mongo-publisher/raw/badges/test-coverage/coverage.svg"></a>

This module read DRS Object information from the DRS DB and populates a Mongo collection with those object details.
The designed purpose of the module is in support of the broader DRS2 OCFL Validation process.

## Usage

```bash
usage: main.py [-h] -e ENVIRONMENT [-f IDS_FILE] [--dryrun] [--limit LIMIT] [--after AFTER] [--batch_size BATCH_SIZE]

Collect DRS Object details from the DRS DB and store them in a Mongo DB.

options:
  -h, --help            show this help message and exit
  -e ENVIRONMENT, --environment ENVIRONMENT
                        Must be one of: dev, qa, prod
  -f IDS_FILE, --ids_file IDS_FILE
                        A list of DRS object ids to process. If not provided, all objects will be processed.
  --dryrun              Run the script in dry run mode without making any changes.
  --limit LIMIT         Limit the number of objects to process. If not provided, all objects will be processed.
  --after AFTER         Process objects after a specific Object ID. If not provided, all objects will be processed.
  --batch_size BATCH_SIZE
                        Number of records to process in a batch. If not provided, the default is 1000.
```

## Notes
Applications that import this component must have a '.env' file in the form of 'env-example'