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# FEI TIA (SER & EMI)

RosettaSciIO can read `.ser` and `.emi` files files created by the FEI (now ThermoFisher) software TEM Imaging & Analysis (TIA), but the reading features are not complete (and probably they will be unless FEI/ThermoFisher releases the specifications of the format). That said we know that this is an important feature and if loading a particular `.ser` or `.emi` file fails for you, please report it as an issue in the issues tracker to make us aware of the problem.

RosettaSciIO (unlike TIA) can read data directly from the `.ser` files. However, by doing so, the information that is stored in the `.emi` file is lost. Therefore, it is strongly recommended to load using the `.emi` file instead.

If several `.ser` files are associated to the `.emi` file being read, all of them will be read and returned as a list.

## API functions

rsciio.tia.file_reader(filename, lazy=False, only_valid_data=True)

  Read sets of `.ser` and `.emi` files from the FEI/ThermoFisher software TIA (TEM Imaging & Analysis).

  Parameters:
    filename `python:str`, `pathlib.Path`

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

    lazy bool, 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.

    only_valid_data bool, Default=True

      For cases, where acquisition of series or linescan data stopped before the end. If True, load only the acquired data. If False, the empty data are filled with zeros.

  Returns:
    `python:list` of `python: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.