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graphcast.util.chunker

Data chunking utilities for efficient file processing.

This module provides utilities for processing large files by breaking them into manageable chunks. It supports various file formats (JSON, JSONL, CSV) and provides both file-based and in-memory chunking capabilities.

Key Components
  • AbstractChunker: Base class for chunking implementations
  • FileChunker: File-based chunking with encoding support
  • TableChunker: CSV/TSV file chunking
  • JsonlChunker: JSON Lines file chunking
  • JsonChunker: JSON file chunking
  • TrivialChunker: In-memory list chunking
  • ChunkerDataFrame: Pandas DataFrame chunking
  • ChunkerFactory: Factory for creating appropriate chunkers
Example

chunker = ChunkerFactory.create_chunker( ... resource="data.json", ... type=ChunkerType.JSON, ... batch_size=1000 ... ) for batch in chunker: ... process_batch(batch)

AbstractChunker

Bases: ABC

Abstract base class for chunking implementations.

This class defines the interface for all chunkers, providing common functionality for batch processing and iteration.

Parameters:

Name Type Description Default
batch_size

Number of items per batch (default: 10)

 10
limit

Maximum number of items to process (default: None)

 None

Attributes:

Name Type Description
units_processed

Number of items processed
batch_size

Size of each batch
limit int | None

Maximum number of items to process
cnt

Current count of processed items
iteration_tried

Whether iteration has been attempted
Source code in graphcast/util/chunker.py 
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class AbstractChunker(abc.ABC):
    """Abstract base class for chunking implementations.

    This class defines the interface for all chunkers, providing common
    functionality for batch processing and iteration.

    Args:
        batch_size: Number of items per batch (default: 10)
        limit: Maximum number of items to process (default: None)

    Attributes:
        units_processed: Number of items processed
        batch_size: Size of each batch
        limit: Maximum number of items to process
        cnt: Current count of processed items
        iteration_tried: Whether iteration has been attempted
    """

    def __init__(self, batch_size=10, limit=None):
        self.units_processed = 0
        self.batch_size = batch_size
        self.limit: int | None = limit
        self.cnt = 0
        self.iteration_tried = False

    def _limit_reached(self):
        """Check if the processing limit has been reached.

        Returns:
            bool: True if limit is reached, False otherwise
        """
        return self.limit is not None and self.cnt >= self.limit

    def __iter__(self):
        """Initialize iteration if not already done.

        Returns:
            self: Iterator instance
        """
        if not self.iteration_tried:
            self._prepare_iteration()
        return self

    def __next__(self):
        """Get the next batch of items.

        Returns:
            list: Next batch of items

        Raises:
            StopIteration: When no more items are available or limit is reached
        """
        batch = self._next_item()
        self.cnt += len(batch)
        if not batch or self._limit_reached():
            raise StopIteration
        return batch

    @abc.abstractmethod
    def _next_item(self):
        """Get the next item or batch of items.

        This method must be implemented by subclasses.

        Returns:
            Any: Next item or batch of items
        """
        pass

    def _prepare_iteration(self):
        """Prepare for iteration.

        This method is called before the first iteration attempt.
        """
        self.iteration_tried = True

__iter__()

Initialize iteration if not already done.

Returns:

Name Type Description
self

Iterator instance
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def __iter__(self):
    """Initialize iteration if not already done.

    Returns:
        self: Iterator instance
    """
    if not self.iteration_tried:
        self._prepare_iteration()
    return self

__next__()

Get the next batch of items.

Returns:

Name Type Description
list

Next batch of items

Raises:

Type Description
StopIteration

When no more items are available or limit is reached
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def __next__(self):
    """Get the next batch of items.

    Returns:
        list: Next batch of items

    Raises:
        StopIteration: When no more items are available or limit is reached
    """
    batch = self._next_item()
    self.cnt += len(batch)
    if not batch or self._limit_reached():
        raise StopIteration
    return batch

ChunkFlusherMono

Monolithic chunk flusher for writing data to files.

This class provides functionality for writing chunks of data to files, with support for file naming and size limits.

Parameters:

Name Type Description Default
target_prefix

Prefix for output files

 required
chunksize

Maximum number of items per file

 required
maxchunks

Maximum number of chunks to write

 None
suffix

File suffix (default: '.json')

 None
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class ChunkFlusherMono:
    """Monolithic chunk flusher for writing data to files.

    This class provides functionality for writing chunks of data to files,
    with support for file naming and size limits.

    Args:
        target_prefix: Prefix for output files
        chunksize: Maximum number of items per file
        maxchunks: Maximum number of chunks to write
        suffix: File suffix (default: '.json')
    """

    def __init__(self, target_prefix, chunksize, maxchunks=None, suffix=None):
        self.target_prefix = target_prefix
        self.acc = []
        self.chunk_count = 0
        self.chunksize = chunksize
        self.maxchunks = maxchunks
        self.iprocessed = 0
        self.suffix = "good" if suffix is None else suffix
        logger.info(f" in flush_chunk {self.chunksize}")

    def flush_chunk(self):
        """Write the current chunk to a file."""
        logger.info(
            f" in flush_chunk: : {len(self.acc)}; chunk count : {self.chunk_count}"
        )
        if len(self.acc) > 0:
            filename = f"{self.target_prefix}#{self.suffix}#{self.chunk_count}.json.gz"
            with gzip.GzipFile(filename, "w") as fout:
                fout.write(json.dumps(self.acc, indent=4).encode("utf-8"))
                logger.info(f" flushed {filename}")
                self.chunk_count += 1
                self.iprocessed += len(self.acc)
                self.acc = []

    def push(self, item):
        """Add an item to the current chunk.

        Args:
            item: Item to add to the chunk
        """
        self.acc.append(item)
        if len(self.acc) >= self.chunksize:
            self.flush_chunk()
            gc.collect()

    def stop(self):
        """Flush any remaining items and close."""
        return self.maxchunks is not None and (self.chunk_count >= self.maxchunks)

    def items_processed(self):
        """Get the total number of items processed.

        Returns:
            int: Number of items processed
        """
        return self.iprocessed

flush_chunk()

Write the current chunk to a file.

Source code in graphcast/util/chunker.py 
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def flush_chunk(self):
    """Write the current chunk to a file."""
    logger.info(
        f" in flush_chunk: : {len(self.acc)}; chunk count : {self.chunk_count}"
    )
    if len(self.acc) > 0:
        filename = f"{self.target_prefix}#{self.suffix}#{self.chunk_count}.json.gz"
        with gzip.GzipFile(filename, "w") as fout:
            fout.write(json.dumps(self.acc, indent=4).encode("utf-8"))
            logger.info(f" flushed {filename}")
            self.chunk_count += 1
            self.iprocessed += len(self.acc)
            self.acc = []

items_processed()

Get the total number of items processed.

Returns:

Name Type Description
int

Number of items processed
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def items_processed(self):
    """Get the total number of items processed.

    Returns:
        int: Number of items processed
    """
    return self.iprocessed

push(item)

Add an item to the current chunk.

Parameters:

Name Type Description Default
item

Item to add to the chunk

 required
Source code in graphcast/util/chunker.py 
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def push(self, item):
    """Add an item to the current chunk.

    Args:
        item: Item to add to the chunk
    """
    self.acc.append(item)
    if len(self.acc) >= self.chunksize:
        self.flush_chunk()
        gc.collect()

stop()

Flush any remaining items and close.

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def stop(self):
    """Flush any remaining items and close."""
    return self.maxchunks is not None and (self.chunk_count >= self.maxchunks)

ChunkerDataFrame

Bases: AbstractChunker

Chunker for Pandas DataFrames.

This class provides chunking functionality for Pandas DataFrames, converting each chunk into a list of dictionaries.

Parameters:

Name Type Description Default
df DataFrame

DataFrame to chunk

 required
**kwargs

Additional arguments for AbstractChunker

 {}
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class ChunkerDataFrame(AbstractChunker):
    """Chunker for Pandas DataFrames.

    This class provides chunking functionality for Pandas DataFrames,
    converting each chunk into a list of dictionaries.

    Args:
        df: DataFrame to chunk
        **kwargs: Additional arguments for AbstractChunker
    """

    def __init__(self, df: pd.DataFrame, **kwargs):
        super().__init__(**kwargs)
        self.df = df
        self.columns = df.columns

    def _next_item(self):
        """Get the next batch of rows as dictionaries.

        Returns:
            list[dict]: Next batch of rows as dictionaries
        """
        cid = self.cnt
        pre_batch = self.df.iloc[cid : cid + self.batch_size].values.tolist()
        batch = [{k: v for k, v in zip(self.columns, item)} for item in pre_batch]
        return batch

ChunkerFactory

Factory for creating appropriate chunkers.

This class provides a factory method for creating chunkers based on the type of resource and configuration provided.

Example

chunker = ChunkerFactory.create_chunker( ... resource="data.json", ... type=ChunkerType.JSON, ... batch_size=1000 ... )

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class ChunkerFactory:
    """Factory for creating appropriate chunkers.

    This class provides a factory method for creating chunkers based on
    the type of resource and configuration provided.

    Example:
        >>> chunker = ChunkerFactory.create_chunker(
        ...     resource="data.json",
        ...     type=ChunkerType.JSON,
        ...     batch_size=1000
        ... )
    """

    @classmethod
    def _guess_chunker_type(cls, filename: Path) -> ChunkerType:
        """Guess the appropriate chunker type based on file extension.

        This method examines the file extension to determine the most appropriate
        chunker type. It supports common file extensions for JSON, JSONL, and CSV/TSV files,
        including compressed versions (e.g., .json.gz, .csv.gz).

        Args:
            filename: Path to the file to analyze

        Returns:
            ChunkerType: Guessed chunker type based on file extension

        Raises:
            ValueError: If file extension is not recognized
        """
        # Get all suffixes and remove compression extensions
        suffixes = filename.suffixes
        base_suffix = next(
            (s for s in suffixes if s.lower() not in (".gz", ".zip")), suffixes[0]
        ).lower()

        if base_suffix == ".json":
            return ChunkerType.JSON
        elif base_suffix == ".jsonl":
            return ChunkerType.JSONL
        elif base_suffix in (".csv", ".tsv", ".txt"):
            return ChunkerType.TABLE
        else:
            raise ValueError(
                f"Could not guess chunker type for file extension: {base_suffix}"
            )

    @classmethod
    def create_chunker(cls, **kwargs) -> AbstractChunker:
        """Create an appropriate chunker for the given resource.

        Args:
            **kwargs: Configuration for the chunker, including:
                resource: Path to file, list, or DataFrame
                type: Type of chunker to create (optional, will be guessed if None)
                batch_size: Size of each batch
                limit: Maximum number of items to process

        Returns:
            AbstractChunker: Appropriate chunker instance

        Raises:
            ValueError: If resource type is not supported or chunker type cannot be guessed
        """
        resource: Path | list[dict] | pd.DataFrame | None = kwargs.pop("resource", None)
        chunker_type = kwargs.pop("type", None)

        if isinstance(resource, list):
            return TrivialChunker(array=resource, **kwargs)
        elif isinstance(resource, pd.DataFrame):
            return ChunkerDataFrame(df=resource, **kwargs)
        elif isinstance(resource, Path):
            if chunker_type is None:
                chunker_type = cls._guess_chunker_type(resource)

            if chunker_type == ChunkerType.JSON:
                return JsonChunker(filename=resource, **kwargs)
            elif chunker_type == ChunkerType.JSONL:
                return JsonlChunker(filename=resource, **kwargs)
            elif chunker_type == ChunkerType.TABLE:
                return TableChunker(filename=resource, **kwargs)
            else:
                raise ValueError(f"Unknown chunker type: {chunker_type}")
        else:
            raise ValueError(f"Unsupported resource type: {type(resource)}")

create_chunker(**kwargs) classmethod

Create an appropriate chunker for the given resource.

Parameters:

Name Type Description Default
**kwargs

Configuration for the chunker, including: resource: Path to file, list, or DataFrame type: Type of chunker to create (optional, will be guessed if None) batch_size: Size of each batch limit: Maximum number of items to process

 {}

Returns:

Name Type Description
AbstractChunker AbstractChunker

Appropriate chunker instance

Raises:

Type Description
ValueError

If resource type is not supported or chunker type cannot be guessed
Source code in graphcast/util/chunker.py 
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@classmethod
def create_chunker(cls, **kwargs) -> AbstractChunker:
    """Create an appropriate chunker for the given resource.

    Args:
        **kwargs: Configuration for the chunker, including:
            resource: Path to file, list, or DataFrame
            type: Type of chunker to create (optional, will be guessed if None)
            batch_size: Size of each batch
            limit: Maximum number of items to process

    Returns:
        AbstractChunker: Appropriate chunker instance

    Raises:
        ValueError: If resource type is not supported or chunker type cannot be guessed
    """
    resource: Path | list[dict] | pd.DataFrame | None = kwargs.pop("resource", None)
    chunker_type = kwargs.pop("type", None)

    if isinstance(resource, list):
        return TrivialChunker(array=resource, **kwargs)
    elif isinstance(resource, pd.DataFrame):
        return ChunkerDataFrame(df=resource, **kwargs)
    elif isinstance(resource, Path):
        if chunker_type is None:
            chunker_type = cls._guess_chunker_type(resource)

        if chunker_type == ChunkerType.JSON:
            return JsonChunker(filename=resource, **kwargs)
        elif chunker_type == ChunkerType.JSONL:
            return JsonlChunker(filename=resource, **kwargs)
        elif chunker_type == ChunkerType.TABLE:
            return TableChunker(filename=resource, **kwargs)
        else:
            raise ValueError(f"Unknown chunker type: {chunker_type}")
    else:
        raise ValueError(f"Unsupported resource type: {type(resource)}")

ChunkerType

Bases: BaseEnum

Types of chunkers supported by the system.

JSON: For JSON files JSONL: For JSON Lines files TABLE: For CSV/TSV files TRIVIAL: For in-memory lists

Source code in graphcast/util/chunker.py 
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class ChunkerType(BaseEnum):
    """Types of chunkers supported by the system.

    JSON: For JSON files
    JSONL: For JSON Lines files
    TABLE: For CSV/TSV files
    TRIVIAL: For in-memory lists
    """

    JSON = "json"
    JSONL = "jsonl"
    TABLE = "table"
    TRIVIAL = "trivial"

FPSmart

Smart file pointer for pattern-based file processing.

This class provides a file-like interface with pattern-based transformation of the data being read.

Parameters:

Name Type Description Default
fp

File pointer to wrap

 required
pattern

Regular expression pattern to match

 required
substitute

String to substitute for matches

 ''
count

Maximum number of substitutions (0 for unlimited)

 0
Source code in graphcast/util/chunker.py 
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class FPSmart:
    """Smart file pointer for pattern-based file processing.

    This class provides a file-like interface with pattern-based
    transformation of the data being read.

    Args:
        fp: File pointer to wrap
        pattern: Regular expression pattern to match
        substitute: String to substitute for matches
        count: Maximum number of substitutions (0 for unlimited)
    """

    def __init__(self, fp, pattern, substitute="", count=0):
        self.fp = fp
        self.pattern = pattern
        self.p = re.compile(self.pattern)
        self.count = count
        self.sub = substitute

    def read(self, n):
        """Read and transform data from the file.

        Args:
            n: Number of bytes to read

        Returns:
            str: Transformed data
        """
        s = self.fp.read(n).decode()
        return self.transform(s).encode()

    def transform(self, s):
        """Transform the data using the pattern.

        Args:
            s: Data to transform

        Returns:
            str: Transformed data
        """
        self.p.search(s)
        r = self.p.sub(self.sub, s, count=self.count)
        return r

    def close(self):
        """Close the underlying file pointer."""
        self.fp.close()

close()

Close the underlying file pointer.

Source code in graphcast/util/chunker.py 
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def close(self):
    """Close the underlying file pointer."""
    self.fp.close()

read(n)

Read and transform data from the file.

Parameters:

Name Type Description Default
n

Number of bytes to read

 required

Returns:

Name Type Description
str

Transformed data
Source code in graphcast/util/chunker.py 
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def read(self, n):
    """Read and transform data from the file.

    Args:
        n: Number of bytes to read

    Returns:
        str: Transformed data
    """
    s = self.fp.read(n).decode()
    return self.transform(s).encode()

transform(s)

Transform the data using the pattern.

Parameters:

Name Type Description Default
s

Data to transform

 required

Returns:

Name Type Description
str

Transformed data
Source code in graphcast/util/chunker.py 
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def transform(self, s):
    """Transform the data using the pattern.

    Args:
        s: Data to transform

    Returns:
        str: Transformed data
    """
    self.p.search(s)
    r = self.p.sub(self.sub, s, count=self.count)
    return r

FileChunker

Bases: AbstractChunker

Base class for file-based chunking.

This class provides functionality for reading and chunking files, with support for different encodings and compression.

Parameters:

Name Type Description Default
filename

Path to the file to process

 required
encoding EncodingType

File encoding (default: UTF_8)

 UTF_8
mode

File mode ('t' for text, 'b' for binary)

 't'
**kwargs

Additional arguments for AbstractChunker

 {}

Attributes:

Name Type Description
filename Path

Path to the file
file_obj TextIO | GzipFile | None

File object for reading
encoding EncodingType | None

File encoding
mode

File mode
Source code in graphcast/util/chunker.py 
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class FileChunker(AbstractChunker):
    """Base class for file-based chunking.

    This class provides functionality for reading and chunking files,
    with support for different encodings and compression.

    Args:
        filename: Path to the file to process
        encoding: File encoding (default: UTF_8)
        mode: File mode ('t' for text, 'b' for binary)
        **kwargs: Additional arguments for AbstractChunker

    Attributes:
        filename: Path to the file
        file_obj: File object for reading
        encoding: File encoding
        mode: File mode
    """

    def __init__(
        self,
        filename,
        encoding: EncodingType = EncodingType.UTF_8,
        mode="t",
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.filename: Path = filename
        self.file_obj: TextIO | gzip.GzipFile | None = None
        self.encoding: EncodingType | None = encoding
        self.mode = mode
        if self.mode == "b":
            self.encoding = None

    def _next_item(self):
        """Get the next line from the file.

        Returns:
            str: Next line from the file

        Raises:
            StopIteration: When end of file is reached
        """
        return next(self.file_obj)

    def _prepare_iteration(self):
        """Open the file for reading.

        Handles both regular and gzipped files.
        """
        super()._prepare_iteration()
        if ".gz" in self.filename.suffixes:
            self.file_obj = gzip.open(
                self.filename.absolute().as_posix(),
                f"r{self.mode}",
                encoding=self.encoding,
            )
        else:
            self.file_obj = open(
                self.filename.absolute().as_posix(),
                f"r{self.mode}",
                encoding=self.encoding,
            )

    def __next__(self):
        """Get the next batch of lines.

        Returns:
            list[str]: Next batch of lines

        Raises:
            StopIteration: When end of file is reached or limit is reached
        """
        batch = []

        if self._limit_reached():
            self.file_obj.close()
            raise StopIteration
        while len(batch) < self.batch_size and not self._limit_reached():
            try:
                batch += [self._next_item()]
                self.cnt += 1
            except StopIteration:
                if batch:
                    return batch
                self.file_obj.close()
                raise StopIteration

        return batch

__next__()

Get the next batch of lines.

Returns:

Type Description

list[str]: Next batch of lines

Raises:

Type Description
StopIteration

When end of file is reached or limit is reached
Source code in graphcast/util/chunker.py 
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def __next__(self):
    """Get the next batch of lines.

    Returns:
        list[str]: Next batch of lines

    Raises:
        StopIteration: When end of file is reached or limit is reached
    """
    batch = []

    if self._limit_reached():
        self.file_obj.close()
        raise StopIteration
    while len(batch) < self.batch_size and not self._limit_reached():
        try:
            batch += [self._next_item()]
            self.cnt += 1
        except StopIteration:
            if batch:
                return batch
            self.file_obj.close()
            raise StopIteration

    return batch

JsonChunker

Bases: FileChunker

Chunker for JSON files.

This class extends FileChunker to handle JSON files using streaming JSON parsing for memory efficiency.

Source code in graphcast/util/chunker.py 
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class JsonChunker(FileChunker):
    """Chunker for JSON files.

    This class extends FileChunker to handle JSON files using
    streaming JSON parsing for memory efficiency.
    """

    def __init__(self, **kwargs):
        super().__init__(mode="b", **kwargs)
        self.parser: Any

    def _prepare_iteration(self):
        """Initialize the JSON parser for streaming."""
        super()._prepare_iteration()
        self.parser = ijson.items(self.file_obj, "item")

    def _next_item(self):
        """Get the next JSON object.

        Returns:
            dict: Next parsed JSON object

        Raises:
            StopIteration: When end of file is reached
        """
        return next(self.parser)

JsonlChunker

Bases: FileChunker

Chunker for JSON Lines files.

This class extends FileChunker to handle JSON Lines format, parsing each line as a JSON object.

Source code in graphcast/util/chunker.py 
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class JsonlChunker(FileChunker):
    """Chunker for JSON Lines files.

    This class extends FileChunker to handle JSON Lines format,
    parsing each line as a JSON object.
    """

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def __next__(self):
        """Get the next batch of JSON objects.

        Returns:
            list[dict]: Next batch of parsed JSON objects
        """
        lines = super().__next__()
        lines2 = [json.loads(line) for line in lines]
        return lines2

__next__()

Get the next batch of JSON objects.

Returns:

Type Description

list[dict]: Next batch of parsed JSON objects
Source code in graphcast/util/chunker.py 
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def __next__(self):
    """Get the next batch of JSON objects.

    Returns:
        list[dict]: Next batch of parsed JSON objects
    """
    lines = super().__next__()
    lines2 = [json.loads(line) for line in lines]
    return lines2

TableChunker

Bases: FileChunker

Chunker for CSV/TSV files.

This class extends FileChunker to handle tabular data, converting each row into a dictionary with column headers as keys.

Parameters:

Name Type Description Default
**kwargs

Arguments for FileChunker, including: sep: Field separator (default: ',')

 {}
Source code in graphcast/util/chunker.py 
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class TableChunker(FileChunker):
    """Chunker for CSV/TSV files.

    This class extends FileChunker to handle tabular data, converting
    each row into a dictionary with column headers as keys.

    Args:
        **kwargs: Arguments for FileChunker, including:
            sep: Field separator (default: ',')
    """

    def __init__(self, **kwargs):
        self.sep = kwargs.pop("sep", ",")
        super().__init__(**kwargs)
        self.header: list[str]

    def _prepare_iteration(self):
        """Read the header row and prepare for iteration."""
        super()._prepare_iteration()
        header = next(self.file_obj)
        self.header = header.rstrip("\n").split(self.sep)

    def __next__(self):
        """Get the next batch of rows as dictionaries.

        Returns:
            list[dict]: Next batch of rows as dictionaries
        """
        lines = super().__next__()
        lines2 = [
            next(csv.reader([line.rstrip()], skipinitialspace=True)) for line in lines
        ]
        dressed = [dict(zip(self.header, row)) for row in lines2]
        return dressed

__next__()

Get the next batch of rows as dictionaries.

Returns:

Type Description

list[dict]: Next batch of rows as dictionaries
Source code in graphcast/util/chunker.py 
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def __next__(self):
    """Get the next batch of rows as dictionaries.

    Returns:
        list[dict]: Next batch of rows as dictionaries
    """
    lines = super().__next__()
    lines2 = [
        next(csv.reader([line.rstrip()], skipinitialspace=True)) for line in lines
    ]
    dressed = [dict(zip(self.header, row)) for row in lines2]
    return dressed

TrivialChunker

Bases: AbstractChunker

Chunker for in-memory lists.

This class provides chunking functionality for lists of dictionaries that are already in memory.

Parameters:

Name Type Description Default
array list[dict]

List of dictionaries to chunk

 required
**kwargs

Additional arguments for AbstractChunker

 {}
Source code in graphcast/util/chunker.py 
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class TrivialChunker(AbstractChunker):
    """Chunker for in-memory lists.

    This class provides chunking functionality for lists of dictionaries
    that are already in memory.

    Args:
        array: List of dictionaries to chunk
        **kwargs: Additional arguments for AbstractChunker
    """

    def __init__(self, array: list[dict], **kwargs):
        super().__init__(**kwargs)
        self.array = array

    def _next_item(self):
        """Get the next batch of items from the array.

        Returns:
            list[dict]: Next batch of items
        """
        return self.array[self.cnt : self.cnt + self.batch_size]

    def __next__(self):
        """Get the next batch of items.

        Returns:
            list[dict]: Next batch of items

        Raises:
            StopIteration: When no more items are available or limit is reached
        """
        batch = self._next_item()
        self.cnt += len(batch)
        if not batch or self._limit_reached():
            raise StopIteration
        return batch

__next__()

Get the next batch of items.

Returns:

Type Description

list[dict]: Next batch of items

Raises:

Type Description
StopIteration

When no more items are available or limit is reached
Source code in graphcast/util/chunker.py 
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def __next__(self):
    """Get the next batch of items.

    Returns:
        list[dict]: Next batch of items

    Raises:
        StopIteration: When no more items are available or limit is reached
    """
    batch = self._next_item()
    self.cnt += len(batch)
    if not batch or self._limit_reached():
        raise StopIteration
    return batch

convert(source, target_root, chunk_size=10000, max_chunks=None, pattern=None, force_list=None, root_tag=None)

Convert XML file to JSON chunks.

This function processes an XML file and converts it to a series of JSON files, with support for pattern-based transformation and chunking.

Parameters:

Name Type Description Default
source Path

Path to source XML file

 required
target_root str

Root path for output files

 required
chunk_size int

Number of items per output file (default: 10000)

 10000
max_chunks

Maximum number of chunks to create (default: None)

 None
pattern str | None

Regular expression pattern for transformation

 None
force_list

List of tags that should always be lists

 None
root_tag

Root tag to start parsing from

 None
Example

convert( ... source="data.xml", ... target_root="output", ... chunk_size=1000, ... pattern=r'xmlns="[^"]*"', ... root_tag="PubmedArticle" ... )

Source code in graphcast/util/chunker.py 
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def convert(
    source: pathlib.Path,
    target_root: str,
    chunk_size: int = 10000,
    max_chunks=None,
    pattern: str | None = None,
    force_list=None,
    root_tag=None,
):
    """Convert XML file to JSON chunks.

    This function processes an XML file and converts it to a series of JSON files,
    with support for pattern-based transformation and chunking.

    Args:
        source: Path to source XML file
        target_root: Root path for output files
        chunk_size: Number of items per output file (default: 10000)
        max_chunks: Maximum number of chunks to create (default: None)
        pattern: Regular expression pattern for transformation
        force_list: List of tags that should always be lists
        root_tag: Root tag to start parsing from

    Example:
        >>> convert(
        ...     source="data.xml",
        ...     target_root="output",
        ...     chunk_size=1000,
        ...     pattern=r'xmlns="[^"]*"',
        ...     root_tag="PubmedArticle"
        ... )
    """
    logger.info(f" chunksize : {chunk_size} | maxchunks {max_chunks} ")

    good_cf = ChunkFlusherMono(target_root, chunk_size, max_chunks)
    bad_cf = ChunkFlusherMono(target_root, chunk_size, max_chunks, suffix="bad")

    if source.suffix == ".gz":
        open_foo: Callable = gzip.open
    elif source.suffix == ".xml":
        open_foo = open
    else:
        raise ValueError("Unknown file type")
    # pylint: disable-next=assignment
    fp: gzip.GzipFile | FPSmart | None

    with (
        open_foo(source, "rb")
        if isinstance(  # type: ignore
            source, pathlib.Path
        )
        else nullcontext() as fp
    ):
        if pattern is not None:
            fp = FPSmart(fp, pattern)
        else:
            fp = fp
        parse_simple(fp, good_cf, force_list, root_tag)

        good_cf.flush_chunk()

        logger.info(f" {good_cf.items_processed()} good records")
        bad_cf.flush_chunk()
        logger.info(f"{bad_cf.items_processed()} bad records")

gunzip_file(fname_in, fname_out)

Decompress a gzipped file.

Parameters:

Name Type Description Default
fname_in

Path to input gzipped file

 required
fname_out

Path to output decompressed file

 required
Source code in graphcast/util/chunker.py 
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def gunzip_file(fname_in, fname_out):
    """Decompress a gzipped file.

    Args:
        fname_in: Path to input gzipped file
        fname_out: Path to output decompressed file
    """
    with gzip.open(fname_in, "rb") as f_in:
        with open(fname_out, "wb") as f_out:
            copyfileobj(f_in, f_out)

nullcontext(enter_result=None)

Context manager that does nothing.

Parameters:

Name Type Description Default
enter_result

Value to return when entering the context

 None

Yields:

Type Description

The enter_result value
Source code in graphcast/util/chunker.py 
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@contextmanager
def nullcontext(enter_result=None):
    """Context manager that does nothing.

    Args:
        enter_result: Value to return when entering the context

    Yields:
        The enter_result value
    """
    yield enter_result

parse_simple(fp, good_cf, force_list=None, root_tag=None)

Parse XML file with simple structure.

Parameters:

Name Type Description Default
fp

File pointer to parse

 required
good_cf

Function to check if an element is valid

 required
force_list

List of tags that should always be lists

 None
root_tag

Root tag to start parsing from

 None

Returns:

Name Type Description
dict

Parsed XML data
Source code in graphcast/util/chunker.py 
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def parse_simple(fp, good_cf, force_list=None, root_tag=None):
    """Parse XML file with simple structure.

    Args:
        fp: File pointer to parse
        good_cf: Function to check if an element is valid
        force_list: List of tags that should always be lists
        root_tag: Root tag to start parsing from

    Returns:
        dict: Parsed XML data
    """
    events = ("start", "end")
    tree = et.iterparse(fp, events)
    context = iter(tree)
    event, root = next(context)
    for event, pub in context:
        if event == "end" and (pub.tag == root_tag if root_tag is not None else True):
            item = et.tostring(pub, encoding="utf8", method="xml").decode("utf")
            obj = xmltodict.parse(
                item,
                force_cdata=True,
                force_list=force_list,
            )
            good_cf.push(obj)
            root.clear()
            if good_cf.stop():
                break