Quantum Detector#

The mib file format is the format from the Quantum Detector software to acquired with the Quantum Detector Merlin camera. It is typically used to store a series of diffraction patterns from scanning transmission electron diffraction measurements. It supports reading data from camera with one or four quadrants.

API functions#

rsciio.quantumdetector.file_reader(filename, lazy=False, chunks='auto', mmap_mode=None, navigation_shape=None, first_frame=None, last_frame=None, distributed=False, print_info=False)#

Read a Quantum Detectors mib file.

If a hdr file with the same file name was saved along the mib file, it will be used to read the metadata.

Parameters:

filenamestr, pathlib.Path: Filename of the file to read or corresponding pathlib.Path.
lazybool, default=False: Whether to open the file lazily or not. The file will stay open until closed in compute() or closed manually. get_file_handle() can be used to access the file handler and close it manually.
chunkstuple of int or None, default=”auto”: The chunks used when reading the data lazily. This argument is passed to the chunks of the dask.array.from_array() function.
mmap_mode{None, “r+”, “r”, “w+”, “c”}, default=None: Argument passed to numpy.memmap. A memory-mapped array is stored on disk, and not directly loaded into memory. However, it can be accessed and sliced like any ndarray. Lazy loading does not support in-place writing (i.e lazy loading and the "r+" mode are incompatible). If None (default), the value is "r" when lazy=True, otherwise it is "c".
navigation_shapetuple or None, default=None: Specify the shape of the navigation space. If None, the navigation shape will be infer from metadata and if not possible, the data will be loaded as a stack with a navigation dimension equal to one.
first_frame, last_frameint or None, default=None: The first/last frame to load. It follows python indexing syntax, i.e. negative integer means reverse indexing. If None, it uses first/last index.
distributedbool, default=False: Whether to load the data using memory-mapping in a way that is compatible with dask-distributed. This can sometimes improve performance when reading large files. And splitting the data loading/processing over multiple workers.
print_infobool: Display information about the mib file.

Returns:

list of dict

List of dictionaries containing the following fields:

‘data’ – multidimensional numpy.ndarray or dask.array.Array
‘axes’ – list of dictionaries describing the axes containing the fields ‘name’, ‘units’, ‘index_in_array’, and either ‘size’, ‘offset’, and ‘scale’ or a numpy array ‘axis’ containing the full axes vector
‘metadata’ – dictionary containing the parsed metadata
‘original_metadata’ – dictionary containing the full metadata tree from the input file

When the file contains several datasets, each dataset will be loaded as separate dictionary.

Notes

In case of interrupted acquisition, only the completed lines are read and the incomplete line are discarded.

When the scanning shape (i. e. navigation shape) is not available from the metadata (for example with acquisition using pixel trigger), the timestamps will be used to guess the navigation shape.

Examples

In case, the navigation shape can’t read from the data itself (for example, type of acquisition unsupported), the navigation_shape can be specified:

>>> from rsciio.quantumdetector import file_reader
>>> s_dict = file_reader("file.mib", navigation_shape=(256, 256))

rsciio.quantumdetector.load_mib_data(path, lazy=False, chunks='auto', mmap_mode=None, navigation_shape=None, first_frame=None, last_frame=None, distributed=False, mib_prop=None, return_headers=False, print_info=False, return_mmap=True)#

Load Quantum Detectors MIB file from a path or a memory buffer.

Parameters:

pathstr or bytes: The path to the mib file, otherwise the memory buffer of the mib file. Lazy loading is not supported with memory buffer.
lazybool, default=False: Whether to open the file lazily or not. The file will stay open until closed in compute() or closed manually. get_file_handle() can be used to access the file handler and close it manually.
chunkstuple of int or None, default=”auto”: The chunks used when reading the data lazily. This argument is passed to the chunks of the dask.array.from_array() function.
mmap_mode{None, “r+”, “r”, “w+”, “c”}, default=None: Argument passed to numpy.memmap. A memory-mapped array is stored on disk, and not directly loaded into memory. However, it can be accessed and sliced like any ndarray. Lazy loading does not support in-place writing (i.e lazy loading and the "r+" mode are incompatible). If None (default), the value is "r" when lazy=True, otherwise it is "c".
navigation_shapetuple or None, default=None: Specify the shape of the navigation space. If None, the navigation shape will be infer from metadata and if not possible, the data will be loaded as a stack with a navigation dimension equal to one.
first_frame, last_frameint or None, default=None: The first/last frame to load. It follows python indexing syntax, i.e. negative integer means reverse indexing. If None, it uses first/last index.
distributedbool, default=False: Whether to load the data using memory-mapping in a way that is compatible with dask-distributed. This can sometimes improve performance when reading large files. And splitting the data loading/processing over multiple workers.
mib_propMIBProperties, default=None: The MIBProperties instance of the file. If None, it will be parsed from the file.
return_headersbool, default=False: If True, also return headers.
print_infobool, default=False: If True, display information when loading the file.
return_mmapbool: If True, return the numpy.memmap object. Default is True.

Returns:

numpy.ndarray or dask.array.Array or numpy.memmap: The data from the mib reshaped according to the navigation_shape argument.

rsciio.quantumdetector.parse_exposures(headers, max_index=10000)#

Parse the exposure time from the header of each frames.

Parameters:

headersbytes str or iterable of bytes str: The headers as a bytes string.
max_indexint: Define the maximum index of the frame to be considered to avoid reading the header of all frames. If -1 (default), all frames will be read.

Returns:

list: The exposure in ms of each frame.

Examples

Use load_mib_data function to the headers and parse the exposures from the headers. By default, reads only the first 10 000 frames.

>>> from rsciio.quantumdetector import load_mib_data, parse_exposures
>>> data, headers = load_mib_data(path, return_headers=True, return_mmap=True)
>>> exposures = parse_exposures(headers)

All frames can be parsed by using max_index=-1:

>>> data, headers = load_mib_data(path, return_headers=True)
>>> timestamps = parse_exposures(headers, max_index=-1)
>>> len(timestamps)
65536

rsciio.quantumdetector.parse_timestamps(headers, max_index=10000)#

Parse the timestamp time from the header of each frames.

Parameters:

headersbytes str or iterable of bytes str: The headers as a bytes string.
max_indexint: Define the maximum index of the frame to be considered to avoid reading the header of all frames. If -1 (default), all frames will be read.

Returns:

list: The timestamp of each frame.

Examples

Use load_mib_data function to get the headers and parse the timestamps from the headers. By default, reads only the first 10 000 frames.

>>> from rsciio.quantumdetector import load_mib_data, parse_exposures
>>> data, header = load_mib_data(path, return_headers=True)
>>> timestamps = parse_timestamps(headers)
>>> len(timestamps)
10000

All frames can be parsed by using max_index=-1:

>>> data, headers = load_mib_data(path, return_headers=True)
>>> timestamps = parse_timestamps(headers, max_index=-1)
>>> len(timestamps)
65536