plasmapdf 0.0.1

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PlasmaPDF Quick Start Guide

PlasmaPDF is a Python library for working with PDF documents, particularly focused on handling annotations and data extraction. This guide will walk you through the basic usage of the library.

Installation

To install PlasmaPDF, use pip:

pip install plasmapdf

Basic Usage

1. Importing the Library

Start by importing the necessary components:

from plasmapdf.models.PdfDataLayer import makePdfTranslationLayerFromPawlsTokens
from plasmapdf.models.types import TextSpan, SpanAnnotation, PawlsPagePythonType

2. Creating a PdfDataLayer

The core of plasmaPDF is the PdfDataLayer class. You create an instance of this class using the makePdfTranslationLayerFromPawlsTokens function:

pawls_tokens: list[PawlsPagePythonType] = [
    {
        "page": {"width": 612, "height": 792, "index": 0},
        "tokens": [
            {"x": 72, "y": 72, "width": 50, "height": 12, "text": "Hello"},
            {"x": 130, "y": 72, "width": 50, "height": 12, "text": "World"}
        ]
    }
]

pdf_data_layer = makePdfTranslationLayerFromPawlsTokens(pawls_tokens)

3. Working with Text Spans

You can extract raw text from a span in the document:

span = TextSpan(id="1", start=0, end=11, text="Hello World")
raw_text = pdf_data_layer.get_raw_text_from_span(span)
print(raw_text)  # Output: "Hello World"

4. Creating Annotations

To create an annotation:

span_annotation = SpanAnnotation(span=span, annotation_label="GREETING")
oc_annotation = pdf_data_layer.create_opencontract_annotation_from_span(span_annotation)

5. Accessing Document Information

You can access various pieces of information about the document:

print(pdf_data_layer.doc_text)  # Full document text
print(pdf_data_layer.human_friendly_full_text)  # Human-readable version of the text
print(pdf_data_layer.page_dataframe)  # DataFrame with page information
print(pdf_data_layer.tokens_dataframe)  # DataFrame with token information

Advanced Usage

Working with Multi-Page Documents

PlasmaPDF can handle multi-page documents. When you create the PdfDataLayer, make sure to include tokens for all pages:

multi_page_pawls_tokens = [
    {
        "page": {"width": 612, "height": 792, "index": 0},
        "tokens": [...]
    },
    {
        "page": {"width": 612, "height": 792, "index": 1},
        "tokens": [...]
    }
]

pdf_data_layer = makePdfTranslationLayerFromPawlsTokens(multi_page_pawls_tokens)

Splitting Spans Across Pages

If you have a span that potentially crosses page boundaries, you can split it:

long_span = TextSpan(id="2", start=0, end=1000, text="...")
page_aware_spans = pdf_data_layer.split_span_on_pages(long_span)

Creating OpenContracts Annotations

To create an annotation in the OpenContracts format:

span = TextSpan(id="3", start=0, end=20, text="Important clause here")
span_annotation = SpanAnnotation(span=span, annotation_label="IMPORTANT_CLAUSE")
oc_annotation = pdf_data_layer.create_opencontract_annotation_from_span(span_annotation)

Utility Functions

PlasmaPDF includes utility functions for working with job results:

from plasmapdf.utils.utils import package_job_results_to_oc_generated_corpus_type

# Assume you have job_results, possible_span_labels, possible_doc_labels, 
# possible_relationship_labels, and suggested_label_set

corpus = package_job_results_to_oc_generated_corpus_type(
    job_results,
    possible_span_labels,
    possible_doc_labels,
    possible_relationship_labels,
    suggested_label_set
)

This function packages job results into the OpenContracts corpus format.

Testing

PlasmaPDF comes with a suite of unit tests. You can run these tests to ensure everything is working correctly:

hatch test

This will run all the tests in the tests directory.

Conclusion

This quick start guide covers the basics of using PlasmaPDF. For more detailed information, refer to the full documentation or explore the source code. If you encounter any issues or have questions, please refer to the project's issue tracker or documentation.