Gatan Digital Micrograph#

RosettaSciIO can read both .dm3 and .dm4 files, but the reading features are not complete (and probably they will remain so, unless Gatan releases the specifications of the format). That said, we understand that this is an important feature and if loading a particular Digital Micrograph file fails for you, please report it as an issue in the issues tracker to make us aware of the problem.

Some of the tags in the DM-files are added to the metadata of the signal object. This includes, microscope information and certain parameters for EELS, EDS and CL signals.


It has been reported that in some versions of Gatan Digital Micrograph, any binned data stores the _averages_ of the binned channels or pixels, rather than the _sum_, which would be required for proper statistical analysis. We therefore strongly recommend that all binning is performed using python, e.g. HyperSpy, where possible.

See the original bug report here.

API functions#

rsciio.digitalmicrograph.file_reader(filename, lazy=False, order=None, optimize=True)#

Read a DM3/4 file and loads the data into the appropriate class.

If more than one dataset is contained in the .dm3/4 file, a list of signals is returned.

filenamestr, pathlib.Path

Filename of the file to read or corresponding pathlib.Path.

lazybool, default=False

Whether to open the file lazily or not.


One of ‘C’ or ‘F’. Define the ordering of the data.

optimizebool, Default=True

If True, the data is replaced by its optimized copy during loading to speed up operations, e.g. iteration over navigation axes. The cost of this speed improvement is to double the memory requirement during data loading, which for large data sets can lead to a slow down on machines with limited memory. When operating on lazy signals, if True, the chunks are optimised for the new axes configuration.

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