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CSVTableSpecParser

dcmspec.csv_table_spec_parser.CSVTableSpecParser

Bases: SpecParser

Base parser for DICOM Specification IHE tables in CSV-like format.

Source code in src/dcmspec/csv_table_spec_parser.py 
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class CSVTableSpecParser(SpecParser):
    """Base parser for DICOM Specification IHE tables in CSV-like format."""

    def parse(
        self,
        table: dict,
        column_to_attr,
        name_attr="elem_name",
        table_id=None,
        include_depth=None,
    ) -> Tuple[Node, Node]:
        """Parse specification metadata and content from a single table dict.

        Args:
            table (dict): A table dict as output by PDFDocHandler.concat_tables, with 'header' and 'data' keys.
            column_to_attr (dict): Mapping from column indices to node attribute names.
            name_attr (str): The attribute to use for node names.
            table_id (str, optional): Table identifier for model parsing.
            include_depth (int, optional): The depth to which included tables should be parsed.

        Returns:
            tuple: (metadata_node, content_node)

        """
        # Use the header and data from the grouped table dict
        header = table.get("header", [])
        data = table.get("data", [])

        metadata = Node("metadata")
        metadata.header = header
        metadata.column_to_attr = column_to_attr
        metadata.table_id = table_id
        if include_depth is not None:
            metadata.include_depth = int(include_depth)
        content = self.parse_table([data], column_to_attr, name_attr)
        return metadata, content

    def parse_table(
        self,
        tables: list,  # List of tables, each a list of rows (list of str)
        column_to_attr: dict,
        name_attr: str = "elem_name",
    ) -> Node:
        """Build a tree from tables using column mapping and '>' nesting logic.

        Args:
            tables (list): List of tables, each a list of rows (list of str).
            column_to_attr (dict): Mapping from column indices to node attribute names.
            name_attr (str): The attribute to use for node names.

        Returns:
            Node: The root node of the tree.

        """
        root = Node("content")
        parent_nodes = {0: root}
        for table in tables:
            for row in table:
                row_data = {}
                for col_idx, attr in column_to_attr.items():
                    value = row[col_idx] if col_idx < len(row) else ""
                    # Clean up newlines in the cell to be used as node name
                    if attr == name_attr:
                        value = value.replace("\n", " ")
                    row_data[attr] = value
                node_name = row_data[name_attr]
                level = node_name.count(">") + 1
                # Ensure all parent levels exist
                if (level - 1) not in parent_nodes:
                    # If a parent is missing, attach to root
                    parent_nodes[level - 1] = root
                parent = parent_nodes[level - 1]
                child = Node(node_name, parent=parent, **row_data)
                parent_nodes[level] = child
        return root

parse(table, column_to_attr, name_attr='elem_name', table_id=None, include_depth=None)

Parse specification metadata and content from a single table dict.

PARAMETER DESCRIPTION
table

A table dict as output by PDFDocHandler.concat_tables, with 'header' and 'data' keys.

TYPE: dict

column_to_attr

Mapping from column indices to node attribute names.

TYPE: dict

name_attr

The attribute to use for node names.

TYPE: str DEFAULT: 'elem_name'

table_id

Table identifier for model parsing.

TYPE: str DEFAULT: None

include_depth

The depth to which included tables should be parsed.

TYPE: int DEFAULT: None

RETURNS DESCRIPTION
tuple

(metadata_node, content_node)

TYPE: Tuple[Node, Node]

Source code in src/dcmspec/csv_table_spec_parser.py 
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def parse(
    self,
    table: dict,
    column_to_attr,
    name_attr="elem_name",
    table_id=None,
    include_depth=None,
) -> Tuple[Node, Node]:
    """Parse specification metadata and content from a single table dict.

    Args:
        table (dict): A table dict as output by PDFDocHandler.concat_tables, with 'header' and 'data' keys.
        column_to_attr (dict): Mapping from column indices to node attribute names.
        name_attr (str): The attribute to use for node names.
        table_id (str, optional): Table identifier for model parsing.
        include_depth (int, optional): The depth to which included tables should be parsed.

    Returns:
        tuple: (metadata_node, content_node)

    """
    # Use the header and data from the grouped table dict
    header = table.get("header", [])
    data = table.get("data", [])

    metadata = Node("metadata")
    metadata.header = header
    metadata.column_to_attr = column_to_attr
    metadata.table_id = table_id
    if include_depth is not None:
        metadata.include_depth = int(include_depth)
    content = self.parse_table([data], column_to_attr, name_attr)
    return metadata, content

parse_table(tables, column_to_attr, name_attr='elem_name')

Build a tree from tables using column mapping and '>' nesting logic.

PARAMETER DESCRIPTION
tables

List of tables, each a list of rows (list of str).

TYPE: list

column_to_attr

Mapping from column indices to node attribute names.

TYPE: dict

name_attr

The attribute to use for node names.

TYPE: str DEFAULT: 'elem_name'

RETURNS DESCRIPTION
Node

The root node of the tree.

TYPE: Node

Source code in src/dcmspec/csv_table_spec_parser.py 
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def parse_table(
    self,
    tables: list,  # List of tables, each a list of rows (list of str)
    column_to_attr: dict,
    name_attr: str = "elem_name",
) -> Node:
    """Build a tree from tables using column mapping and '>' nesting logic.

    Args:
        tables (list): List of tables, each a list of rows (list of str).
        column_to_attr (dict): Mapping from column indices to node attribute names.
        name_attr (str): The attribute to use for node names.

    Returns:
        Node: The root node of the tree.

    """
    root = Node("content")
    parent_nodes = {0: root}
    for table in tables:
        for row in table:
            row_data = {}
            for col_idx, attr in column_to_attr.items():
                value = row[col_idx] if col_idx < len(row) else ""
                # Clean up newlines in the cell to be used as node name
                if attr == name_attr:
                    value = value.replace("\n", " ")
                row_data[attr] = value
            node_name = row_data[name_attr]
            level = node_name.count(">") + 1
            # Ensure all parent levels exist
            if (level - 1) not in parent_nodes:
                # If a parent is missing, attach to root
                parent_nodes[level - 1] = root
            parent = parent_nodes[level - 1]
            child = Node(node_name, parent=parent, **row_data)
            parent_nodes[level] = child
    return root