Metadata-Version: 1.2
Name: mailprep
Version: 0.1.0
Summary: mailprep converts vCard data into physical labels from SVG templates
Home-page: https://git.sr.ht/~lucidone/mailprep
Author: NE Automation
Author-email: code@neautomation.com
Maintainer: NE Automation
Maintainer-email: code@neautomation.com
License: MIT/Apache-2.0
Description: # mailprep
        
        [![builds.sr.ht status](https://builds.sr.ht/~lucidone/mailprep/.build.yml.svg)](https://builds.sr.ht/~lucidone/mailprep/.build.yml?)
        
        -----
        
        **Table of Contents**
        
        * [Overview](#overview)
        * [Usage](#usage)
        * [Installation](#installation)
        * [Testing](#testing)
        * [Templates](#templates)
        * [License](#license)
        
        ## Overview
        
        `mailprep` converts [vCard](https://en.wikipedia.org/wiki/VCard) data into physical labels with a *Dymo LabelWriter 4XL*.
        
        ## Usage
        
        ```console
        $ mailprep --help
        Usage: mailprep [OPTIONS] VCF_FILEPATH [TEMPLATE_FILEPATH]
        
        Options:
          --printer TEXT   Printer Name
          --count INTEGER  number of labels to print
          --simulate       Generate output PDF without printing
          --help           Show this message and exit.
        ```
        
        The default template is designed for 2.25" × 1.25" Uline S-12996 labels.
        
        ## Installation
        
        mailprep is distributed on [PyPI](https://pypi.org) as a universal
        wheel and is available on Linux/macOS and Windows and supports
        Python 3.5+ and PyPy.
        
        ```console
        $ pip install mailprep
        ```
        
        ### Debian
        
        The DYMO printer driver can be installed with
        
        ```console
        $ apt-get install printer-driver-dymo
        ```
        
        
        ## Testing
        
        ### System Dependencies
        
        #### Debian
        
        Testing requires `pdftotext`
        
        ```console
        $ apt-get install poppler-utils
        ```
        
        ### Automatic Tests
        Automatic tests can be run via any of the following methods depending on your workflow
        
        ```console
        $ python setup.py test
        ```
        
        ```console
        $ hatch test
        ```
        
        ```console
        $ tox
        ```
        
        ### HitL Tests
        Human/Hardware in the Loop tests can be run manually if `evince` is installed and a printer is connected.
        
        ```console
        $ hatch test --test-args "--hitl"
        ```
        
        ```console
        $ tox -- --hitl
        ```
        
        ## Templates
        Templates are stored as [SVG](https://en.wikipedia.org/wiki/Scalable_Vector_Graphics) and are evaluated using the Moustache template syntax. Currently the template processing is US-centric, but pull requests and test data is appreciated.
        
        ### Formatted name
        `{{ fn }}` in the template is replaced with the [formatted name](https://tools.ietf.org/html/rfc2426#section-3.1.1) from the vCard.
        
        ### Address
        Currently `mailprep` generate labels from the vCard [ADR Type Definition](https://tools.ietf.org/html/rfc2426#section-3.2.1). In the future it may make more sense to use the [LABEL Type Definition](https://tools.ietf.org/html/rfc2426#section-3.2.2) but it is unclear which produced more consistent results.
        
        ```
        {{ adr_street }}
        {{ adr_city }}, {{ adr_region }}
        {{ adr_code }}
        ```
        
        ## License
        
        mailprep is distributed under the terms of both
        
        - [MIT License](https://choosealicense.com/licenses/mit)
        - [Apache License, Version 2.0](https://choosealicense.com/licenses/apache-2.0)
        
        at your option.
        
        ### Test data
        The vCard test data is from [Wikipedia](https://en.wikipedia.org/wiki/Vcard#vCard_3.0)
        and is licensed as [Creative Commons Attribution-ShareAlike](https://en.wikipedia.org/wiki/Wikipedia:Text_of_Cre
        ative_Commons_Attribution-ShareAlike_3.0_Unported_License).
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
