pyTEMlib , a pycroscopy library

Open Files¶

a pycroscopy ecosystem package

Notebook by Gerd Duscher, 2025

Microscopy Facilities
Institute of Advanced Materials & Manufacturing
The University of Tennessee, Knoxville

Model based analysis and quantification of data acquired with transmission electron microscopes

Scope¶

This notebook shows how to open a file, and plot datasets. Files with more than one dataset (for examples spectrum images) are handled in an intuitive way.

Note: You can combine and arrange these datasets yourself to your liking.

All ususal file-types used in microscopy are handled.

Check Installtion¶

In google colab, with always a clean, new python installation, you will have to run this every (run)time.

import sys
import importlib.metadata
def test_package(package_name):
    """Test if package exists and returns version or -1"""
    try:
        version = importlib.metadata.version(package_name)
    except importlib.metadata.PackageNotFoundError:
        version = '-1'
    return version

if test_package('pyTEMlib') < '0.2025.12.1':
    print('installing pyTEMlib')
    !{sys.executable} -m pip install  --upgrade pyTEMlib -q

print('done')

Load Necessary Packages¶

We will generally need:

numpy: the numeric library
matplotlib: the plotting library activated for jupyter notebooks (%matplotlib widget)
pyTEMlib

%matplotlib widget
import matplotlib.pyplot as plt
import numpy as np

import pyTEMlib
print('pyTEMlib version', pyTEMlib.__version__)

pyTEMlib version 0.2025.12.0

Open File and Plot¶

The reading of files is enabled by SciFiReaders package of the pycroscopy ecosystem.

All files are internally converted to the sidpy format in memory. This allows the usage of any package in pycroscopy including plotting.

The datasets are always organized in python dictionaries (dict), a container with a key and a dataset.

Anything can be added to such a dictionary and then be saved. This alllows for the flexible dataset structure necessary for microscopy.

Chose Filee¶

The widget started below allows to choose a file even in google colab.

The opening and reading of files is enabled by the file_tools library of pyTEMlib

Select Main after you navigated to the file to view.

Adding another file to the dataset dictionary is helpful

file_list = pyTEMlib.file_tools.FileWidget()

Plotting the dataset¶

Once you have selected a dataset, we can plot it. If there is more than one dataset in the file you can select the primary one in the drop down menu next to the buttons

Note:
Plotting comand is independent of dataset type. This type can be set in a sidpy dataset.

dataset_collection = file_list.datasets

dataset = file_list.selected_dataset
view = dataset.plot()

### Select a different dataset

dataset_chooser = pyTEMlib.file_tools.ChooseDataset(dataset_collection)

view = dataset_chooser.dataset.plot()

Save¶

All datasets in a dictionary (here: dataset_collection) can be saved in one file

Check out the different datasets

dataset_collection.keys()

dict_keys(['Channel_000', 'Channel_001', 'Channel_002', 'Channel_003'])

The data are saved in the NSID data format, a format based on hdf5, that is readable on any computer including supercomputers.

The format is NEXUS compatible.

group = pyTEMlib.file_tools.save_dataset(dataset_collection, filename=dataset.metadata['filename'])

C:\Users\gduscher\AppData\Local\anaconda3\Lib\site-packages\pyNSID\io\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version
  warn('validate_h5_dimension may be removed in a future version',
C:\Users\gduscher\AppData\Local\anaconda3\Lib\site-packages\pyNSID\io\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version
  warn('validate_h5_dimension may be removed in a future version',
C:\Users\gduscher\AppData\Local\anaconda3\Lib\site-packages\pyNSID\io\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version
  warn('validate_h5_dimension may be removed in a future version',
C:\Users\gduscher\AppData\Local\anaconda3\Lib\site-packages\pyNSID\io\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version
  warn('validate_h5_dimension may be removed in a future version',

Summary¶

Any common microscopy dataset can be read and plotted.

The datasets are represented in a consistent way in memory and any package of pycroscopy can be used with those.