Provides some helper functions to extract and view parameters stored in the header of SEM images (.tif) recorded using either Zeiss SmartSEM or Thermo Fisher Scientific xT software.

This is a single Python module and can either be installed or easily incorporated into other projects (the license must be retained in this case).

Please clone/download the repository and install with pip

cd sem_io
pip install .

Command line

To print an overview of parameters from the image header in the console, at the command line, you can do:

sem_io path_to_my_image.tif

Python

You can also import the module and use the functions directly in Python.

>>> import sem_io

To print an overview of parameters from the image header in the console:

>>> my_params = sem_io.SEMparams("path_to_my_image.tif")

If you just want to collect and store the parameters and not print them, you can do:

>>> my_params = sem_io.SEMparams("path_to_my_image.tif", verbose=False)

You can print ALL the header parameters to the console like this:

>>> my_params.print_param_dict(my_params.params)

You can print A SELECTION OF the header parameters (given in the class definition) to the console like this:

>>> my_params.print_param_dict(my_params.params_grouped)

Both my_params.params and my_params.params_groups are dictionaries and any parameter can be accessed, e.g.

>>> date = my_params.params_grouped["General"]["Date"]

Alternatively you can dump all the header parameters to a json file (optionally including a key giving the original image path) like this:

>>> my_params.dump_params_to_json(my_params.params, "my_json_path.json", image_path=my_params.img_path)

All the functions are staticmethods, so you don't need to instantiate the SEMparams class at all. For example, there is a bespoke function for getting the image pixel size and its unit in one line of code:

>>> pixel_size, unit = sem_io.SEMparams.get_image_pixel_size("path_to_my_image.tif")

This is useful if you want to plot the SEM image using matplotlib and add a scalebar with the correct dimensions using matplotlib-scalebar. Here's the whole process as an example:

>>> import matplotlib.pyplot as plt
>>> from matplotlib_scalebar.scalebar import ScaleBar
>>> import sem_io
>>> my_image = plt.imread("path_to_my_image.tif")
>>> fig, ax = plt.subplots()
>>> ax.imshow(my_image, cmap='gray')
>>> pixel_size, unit = sem_io.SEMparams.get_image_pixel_size("path_to_my_image.tif")
>>> my_scalebar = ScaleBar(pixel_size, units=unit, location='lower right', scale_loc='top')
>>> ax.add_artist(my_scalebar)

Even Electron Channeling Patterns acquired in rocking beam mode are correctly handled by sem_io.SEMparams.get_image_pixel_size()

• Pillow

• The selected parameters defined in the class definition of SEMparams form a subset of those available in the header of the .tif image. If you are interested in other parameters, the program can be easily customised - all the header parameters are extracted and are available as the "params" instance attribute.
• If there are any issues, please feel free to get in touch using the issues mechanism