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Sample Logger

rfsoc_rfdc.sample_logger

Classes

HDF5Logger

logging using HDF5 format for efficient storage of large arrays.

Advantages: - Automatic chunking and compression - Partial read/write without loading entire file - Stores metadata alongside data - Industry standard format

Requires: h5py (pip install h5py)

Source code in rfsoc_rfdc/sample_logger.py 
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class HDF5Logger:
    """
    logging using HDF5 format for efficient storage of large arrays.

    Advantages:
    - Automatic chunking and compression
    - Partial read/write without loading entire file
    - Stores metadata alongside data
    - Industry standard format

    Requires: h5py (pip install h5py)
    """

    def __init__(self, compression: str = 'gzip', compression_opts: int = 4):
        """
        Initialize HDF5 logging.

        Args:
            compression: Compression algorithm ('gzip', 'lzf', or None)
            compression_opts: Compression level (0-9 for gzip)
        """
        try:
            import h5py
            self.h5py = h5py
        except ImportError:
            raise ImportError(
                "h5py is required for HDF5Logger. Install with: pip install h5py"
            )

        self.compression = compression
        self.compression_opts = compression_opts

    def save(self, data: np.ndarray, filename: str,
             add_timestamp: bool = True,
             dataset_name: str = 'iq_samples',
             metadata: Optional[dict] = None) -> str:
        """
        Save numpy array to HDF5 file.

        Args:
            data: Numpy array to save
            filename: Output filename (without extension)
            add_timestamp: Whether to append timestamp to filename
            dataset_name: Name of dataset within HDF5 file
            metadata: Optional dictionary of metadata to store

        Returns:
            Path to saved HDF5 file
        """
        # Prepare filename
        if add_timestamp:
            timestamp = time.strftime("%Y%m%d_%H%M%S")
            filename = f"{filename}_{timestamp}"

        if not filename.endswith('.h5'):
            filename = f"{filename}.h5"

        # Save to HDF5
        with self.h5py.File(filename, 'w') as f:
            # Create dataset with chunking and compression
            f.create_dataset(
                dataset_name,
                data=data,
                compression=self.compression,
                compression_opts=self.compression_opts if self.compression == 'gzip' else None,
                chunks=True
            )

            # Store metadata as attributes
            f[dataset_name].attrs['timestamp'] = time.strftime(
                "%Y-%m-%d %H:%M:%S")
            f[dataset_name].attrs['shape'] = data.shape
            f[dataset_name].attrs['dtype'] = str(data.dtype)

            if metadata:
                for key, value in metadata.items():
                    f[dataset_name].attrs[key] = value

        file_size_mb = Path(filename).stat().st_size / (1024 * 1024)
        logging.info(f"Saved {data.nbytes / (1024**2):.2f} MB data to {filename} "
                     f"(compressed to {file_size_mb:.2f} MB)")

        return filename

    def load(self, filename: str, dataset_name: str = 'iq_samples') -> tuple:
        """
        Load numpy array from HDF5 file.

        Args:
            filename: HDF5 file path
            dataset_name: Name of dataset to load

        Returns:
            Tuple of (data, metadata)
        """
        with self.h5py.File(filename, 'r') as f:
            data = f[dataset_name][:]
            metadata = dict(f[dataset_name].attrs)

        logging.info(f"Loaded dataset '{dataset_name}' from {filename}")
        return data, metadata

    def load_partial(self, filename: str, start: int, end: int,
                     dataset_name: str = 'iq_samples') -> np.ndarray:
        """
        Load partial data from HDF5 file without loading entire array.

        Args:
            filename: HDF5 file path
            start: Start index
            end: End index
            dataset_name: Name of dataset to load

        Returns:
            Partial numpy array
        """
        with self.h5py.File(filename, 'r') as f:
            data = f[dataset_name][start:end]

        return data
Functions
save(data, filename, add_timestamp=True, dataset_name='iq_samples', metadata=None)

Save numpy array to HDF5 file.

Parameters:

Name Type Description Default
data ndarray

Numpy array to save

 required
filename str

Output filename (without extension)

 required
add_timestamp bool

Whether to append timestamp to filename

 True
dataset_name str

Name of dataset within HDF5 file

 'iq_samples'
metadata Optional[dict]

Optional dictionary of metadata to store

 None

Returns:

Type Description
str

Path to saved HDF5 file
Source code in rfsoc_rfdc/sample_logger.py 
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def save(self, data: np.ndarray, filename: str,
         add_timestamp: bool = True,
         dataset_name: str = 'iq_samples',
         metadata: Optional[dict] = None) -> str:
    """
    Save numpy array to HDF5 file.

    Args:
        data: Numpy array to save
        filename: Output filename (without extension)
        add_timestamp: Whether to append timestamp to filename
        dataset_name: Name of dataset within HDF5 file
        metadata: Optional dictionary of metadata to store

    Returns:
        Path to saved HDF5 file
    """
    # Prepare filename
    if add_timestamp:
        timestamp = time.strftime("%Y%m%d_%H%M%S")
        filename = f"{filename}_{timestamp}"

    if not filename.endswith('.h5'):
        filename = f"{filename}.h5"

    # Save to HDF5
    with self.h5py.File(filename, 'w') as f:
        # Create dataset with chunking and compression
        f.create_dataset(
            dataset_name,
            data=data,
            compression=self.compression,
            compression_opts=self.compression_opts if self.compression == 'gzip' else None,
            chunks=True
        )

        # Store metadata as attributes
        f[dataset_name].attrs['timestamp'] = time.strftime(
            "%Y-%m-%d %H:%M:%S")
        f[dataset_name].attrs['shape'] = data.shape
        f[dataset_name].attrs['dtype'] = str(data.dtype)

        if metadata:
            for key, value in metadata.items():
                f[dataset_name].attrs[key] = value

    file_size_mb = Path(filename).stat().st_size / (1024 * 1024)
    logging.info(f"Saved {data.nbytes / (1024**2):.2f} MB data to {filename} "
                 f"(compressed to {file_size_mb:.2f} MB)")

    return filename
load(filename, dataset_name='iq_samples')

Load numpy array from HDF5 file.

Parameters:

Name Type Description Default
filename str

HDF5 file path

 required
dataset_name str

Name of dataset to load

 'iq_samples'

Returns:

Type Description
tuple

Tuple of (data, metadata)
Source code in rfsoc_rfdc/sample_logger.py 
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def load(self, filename: str, dataset_name: str = 'iq_samples') -> tuple:
    """
    Load numpy array from HDF5 file.

    Args:
        filename: HDF5 file path
        dataset_name: Name of dataset to load

    Returns:
        Tuple of (data, metadata)
    """
    with self.h5py.File(filename, 'r') as f:
        data = f[dataset_name][:]
        metadata = dict(f[dataset_name].attrs)

    logging.info(f"Loaded dataset '{dataset_name}' from {filename}")
    return data, metadata
load_partial(filename, start, end, dataset_name='iq_samples')

Load partial data from HDF5 file without loading entire array.

Parameters:

Name Type Description Default
filename str

HDF5 file path

 required
start int

Start index

 required
end int

End index

 required
dataset_name str

Name of dataset to load

 'iq_samples'

Returns:

Type Description
ndarray

Partial numpy array
Source code in rfsoc_rfdc/sample_logger.py 
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def load_partial(self, filename: str, start: int, end: int,
                 dataset_name: str = 'iq_samples') -> np.ndarray:
    """
    Load partial data from HDF5 file without loading entire array.

    Args:
        filename: HDF5 file path
        start: Start index
        end: End index
        dataset_name: Name of dataset to load

    Returns:
        Partial numpy array
    """
    with self.h5py.File(filename, 'r') as f:
        data = f[dataset_name][start:end]

    return data

SampleLogger

High-level interface for logging large IQ sample datasets using HDF5.

Supports async logging to avoid blocking data acquisition.

Source code in rfsoc_rfdc/sample_logger.py 
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class SampleLogger:
    """
    High-level interface for logging large IQ sample datasets using HDF5.

    Supports async logging to avoid blocking data acquisition.
    """

    def __init__(self,
                 backend: str = 'hdf5',
                 compression: bool = True,
                 async_write: bool = True):
        """
        Initialize sample logging.

        Args:
            backend: 'hdf5' or 'auto' (both force HDF5)
            compression: Enable gzip compression for HDF5 datasets
            async_write: Write files asynchronously in background thread
        """
        if backend not in {'hdf5', 'auto'}:
            raise ValueError(
                f"Unknown backend: {backend}. Only 'hdf5' is supported.")

        self.async_write = async_write
        self.compression = compression
        self._write_threads = []

        # Always use HDF5 backend
        self.backend = HDF5Logger(
            compression='gzip' if compression else None,
            compression_opts=4
        )
        self.backend_name = 'hdf5'
        logging.info("Using HDF5 backend")

    def save(self, data: np.ndarray, filename: str,
             metadata: Optional[dict] = None,
             add_timestamp: bool = True) -> str:
        """
        Save IQ samples to disk.

        Args:
            data: Numpy array of IQ samples
            filename: Base filename or path (include channel ID in the name if needed)
            metadata: Optional metadata dictionary
            add_timestamp: Whether to append timestamp

        Returns:
            Path to saved file/directory
        """
        # Log data size
        size_mb = data.nbytes / (1024 * 1024)
        logging.info(f"Saving {size_mb:.2f} MB of IQ samples...")

        # Save data
        if self.async_write:
            # Async write in background thread
            thread = threading.Thread(
                target=self._async_save_worker,
                args=(data.copy(), filename, metadata, add_timestamp)
            )
            thread.daemon = False
            thread.start()
            self._write_threads.append(thread)
            logging.info(f"Started async write for {filename}")
            return filename
        else:
            # Synchronous write
            return self._do_save(data, filename, metadata, add_timestamp)

    def _async_save_worker(self, data, filename, metadata, add_timestamp):
        """Worker function for async writes."""
        try:
            self._do_save(data, filename, metadata, add_timestamp)
        except Exception as e:
            logging.error(f"Async write failed: {e}", exc_info=True)

    def _do_save(self, data, filename, metadata, add_timestamp):
        """Actual save implementation."""
        return self.backend.save(
            data,
            filename,
            add_timestamp=add_timestamp,
            metadata=metadata
        )

    def wait_for_writes(self):
        """Wait for all async write operations to complete."""
        if self._write_threads:
            logging.info(
                f"Waiting for {len(self._write_threads)} write operations...")
            for thread in self._write_threads:
                thread.join()
            self._write_threads.clear()
            logging.info("All writes completed")

    def load(self, path: str, dataset_name: str = 'iq_samples'):
        """
        Load IQ samples from disk.

        Args:
            path: File or directory path
            dataset_name: Dataset name (HDF5 only)

        Returns:
            Numpy array or tuple of (data, metadata) for HDF5
        """
        path_obj = Path(path)

        if path_obj.suffix != '.h5':
            raise ValueError(
                f"SampleLogger now only supports HDF5 files (.h5). Invalid path: {path}"
            )

        return HDF5Logger().load(path, dataset_name)
Functions
save(data, filename, metadata=None, add_timestamp=True)

Save IQ samples to disk.

Parameters:

Name Type Description Default
data ndarray

Numpy array of IQ samples

 required
filename str

Base filename or path (include channel ID in the name if needed)

 required
metadata Optional[dict]

Optional metadata dictionary

 None
add_timestamp bool

Whether to append timestamp

 True

Returns:

Type Description
str

Path to saved file/directory
Source code in rfsoc_rfdc/sample_logger.py 
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def save(self, data: np.ndarray, filename: str,
         metadata: Optional[dict] = None,
         add_timestamp: bool = True) -> str:
    """
    Save IQ samples to disk.

    Args:
        data: Numpy array of IQ samples
        filename: Base filename or path (include channel ID in the name if needed)
        metadata: Optional metadata dictionary
        add_timestamp: Whether to append timestamp

    Returns:
        Path to saved file/directory
    """
    # Log data size
    size_mb = data.nbytes / (1024 * 1024)
    logging.info(f"Saving {size_mb:.2f} MB of IQ samples...")

    # Save data
    if self.async_write:
        # Async write in background thread
        thread = threading.Thread(
            target=self._async_save_worker,
            args=(data.copy(), filename, metadata, add_timestamp)
        )
        thread.daemon = False
        thread.start()
        self._write_threads.append(thread)
        logging.info(f"Started async write for {filename}")
        return filename
    else:
        # Synchronous write
        return self._do_save(data, filename, metadata, add_timestamp)
wait_for_writes()

Wait for all async write operations to complete.

Source code in rfsoc_rfdc/sample_logger.py 
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def wait_for_writes(self):
    """Wait for all async write operations to complete."""
    if self._write_threads:
        logging.info(
            f"Waiting for {len(self._write_threads)} write operations...")
        for thread in self._write_threads:
            thread.join()
        self._write_threads.clear()
        logging.info("All writes completed")
load(path, dataset_name='iq_samples')

Load IQ samples from disk.

Parameters:

Name Type Description Default
path str

File or directory path

 required
dataset_name str

Dataset name (HDF5 only)

 'iq_samples'

Returns:

Type Description

Numpy array or tuple of (data, metadata) for HDF5
Source code in rfsoc_rfdc/sample_logger.py 
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def load(self, path: str, dataset_name: str = 'iq_samples'):
    """
    Load IQ samples from disk.

    Args:
        path: File or directory path
        dataset_name: Dataset name (HDF5 only)

    Returns:
        Numpy array or tuple of (data, metadata) for HDF5
    """
    path_obj = Path(path)

    if path_obj.suffix != '.h5':
        raise ValueError(
            f"SampleLogger now only supports HDF5 files (.h5). Invalid path: {path}"
        )

    return HDF5Logger().load(path, dataset_name)

Functions

save_to_file_chunked(data, filename)

Convenience function to save large IQ samples using the HDF5 backend.

Kept for backward compatibility with older code that expected chunked numpy output, but now always writes HDF5 files for better performance.

Parameters:

Name Type Description Default
data ndarray

Numpy array to save

 required
filename str

Base filename (without extension, include channel ID in name if needed)

 required

Returns:

Type Description
str

Path to saved file
Source code in rfsoc_rfdc/sample_logger.py 
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def save_to_file_chunked(data: np.ndarray, filename: str) -> str:
    """
    Convenience function to save large IQ samples using the HDF5 backend.

    Kept for backward compatibility with older code that expected chunked
    numpy output, but now always writes HDF5 files for better performance.

    Args:
        data: Numpy array to save
        filename: Base filename (without extension, include channel ID in name if needed)

    Returns:
        Path to saved file
    """
    logging = SampleLogger(backend='hdf5', async_write=False)
    return logging.save(data, filename)