Adaptive Fourier Filtering

Adaptive Fourier Filtering#

part of

pyTEMlib

a pycroscopy ecosystem package

Notebook by Gerd Duscher, 2023

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

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

Content#

An introduction into Fourier Filtering of images.

Install pyTEMlib#

If you have not done so in the Introduction Notebook, please test and install pyTEMlib and other important packages with the code cell below.

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') < '0.2025.3.0':
    print('installing pyTEMlib')
    !{sys.executable} -m pip install  --upgrade pyTEMlib -q
print('done')

done

Loading of necessary libraries#

Please note, that we only need to load the pyTEMlib library, which is based on sidpy Datasets.

%matplotlib widget
import numpy as np
import matplotlib.pylab as plt 
import sys

if 'google.colab' in sys.modules:
    from google.colab import output
    output.enable_custom_widget_manager()
    from google.colab import drive

import sys
sys.path.insert(0, '../../')
sys.path.insert(0, '../../../sidpy')
import sidpy

%load_ext autoreload
%autoreload 2

import pyTEMlib
import pyTEMlib.file_tools      # File input/ output library
import pyTEMlib.image_tools 

if 'google.colab' in sys.modules:
    drive.mount("/content/drive")
    
print('pyTEMlib version: ', pyTEMlib.__version__)
note_book_version = '2025.05.19'
note_book_name='pyTEMib/notebooks/Imaging/Adaptive_Fourier_Filter'

You don't have igor2 installed.     If you wish to open igor files, you will need to install it     (pip install igor2) before attempting.
You don't have gwyfile installed.     If you wish to open .gwy files, you will need to      install it (pip install gwyfile) before attempting.
Symmetry functions of spglib enabled
Qt dialogs are not available
SimpleITK not installed; Registration Functions for Image Stacks not available
install with: conda install -c simpleitk simpleitk 
pyTEMlib version:  0.2025.04.2

Open File#

The data are held in the memory in the sidpy format with rather extensive capabilities.

All results can be stored in that NSID format file.

First we select the file and the dataset (the dropdown menu at the bottom), in case there are several.

fileWidget = pyTEMlib.file_tools.FileWidget()

Now, we open and plot the selected dataset. Select another one and rerun only cell below (Home button gets you back to original).

Please note that you can zoom and drag the intensity values in the color-bar.

datasets = fileWidget.datasets
dataset = fileWidget.selected_dataset

if dataset.data_type.name != 'IMAGE':
    print('We really would need an image here')

view = dataset.plot()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[4], line 2
      1 datasets = fileWidget.datasets
----> 2 dataset = fileWidget.selected_dataset
      4 if dataset.data_type.name != 'IMAGE':
      5     print('We really would need an image here')

AttributeError: 'FileWidget' object has no attribute 'selected_dataset'

#power_spectrum.add_provenance('jup', 'kkle', 1)
print(dataset.provenance)
im = dataset.sum(axis=0)
im.provenance

{'sidpy': {'from_array_': '_0.12.8_2025-03-20-08:38:20.367207'}, 'open_file_2025-03-20-08:38:21.093136': {'package': 'pyTEMlib', 'version': '0.2025.03.0', 'linked_data': 'SciFiReader.EMDReader'}, 'sum_2025-03-20-08:38:28.246056': {'package': 'sidpy', 'version': '0.12.8'}}

{'sidpy': {'from_array_': '_0.12.8_2025-03-20-08:38:20.367207'},
 'open_file_2025-03-20-08:38:21.093136': {'package': 'pyTEMlib',
  'version': '0.2025.03.0',
  'linked_data': 'SciFiReader.EMDReader'},
 'sum_2025-03-20-08:38:28.246056': {'package': 'sidpy', 'version': '0.12.8'},
 'sum_2025-03-20-10:48:06.952517': {'package': 'sidpy', 'version': '0.12.8'}}

Power Spectrum of Image#

if dataset.data_type.name == 'IMAGE_STACK':
  stack_dim = dataset.get_dimensions_by_type('TEMPORAL')
  image = dataset.sum(axis=stack_dim)
  image.data_type = 'Image'
elif dataset.data_type.name == 'IMAGE':
  image = dataset
else:
  print('We really would need an image here')

power_spectrum = pyTEMlib.image_tools.power_spectrum(image, smoothing=1)
power_spectrum.view_metadata()
print('source: ', power_spectrum.source)
view = power_spectrum.plot()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[4], line 10
      7 else:
      8   print('We really would need an image here')
---> 10 power_spectrum = pyTEMlib.image_tools.power_spectrum(image, smoothing=1)
     11 power_spectrum.view_metadata()
     12 print('source: ', power_spectrum.source)

File ~\OneDrive - University of Tennessee\GitHub\pyTEMlib\notebooks\Imaging\../..\pyTEMlib\image_tools.py:257, in power_spectrum(dset, smoothing)
    239 def power_spectrum(dset, smoothing=3):
    240     """
    241     Calculate power spectrum
    242 
   (...)    254 
    255     """
--> 257     fft_transform = fourier_transform(dset)  # dset.fft()
    258     fft_mag = np.abs(fft_transform)
    259     fft_mag2 = ndimage.gaussian_filter(fft_mag, sigma=(smoothing, smoothing), order=0)

File ~\OneDrive - University of Tennessee\GitHub\pyTEMlib\notebooks\Imaging\../..\pyTEMlib\image_tools.py:215, in fourier_transform(dset)
    211 new_image = new_image - new_image.min()
    213 fft_transform = (np.fft.fftshift(np.fft.fft2(new_image)))
--> 215 image_dims = dset.get_image_dims(return_axes=True)
    217 units_x = '1/' + dset._axes[image_dims[0]].units
    218 units_y = '1/' + dset._axes[image_dims[1]].units

TypeError: Dataset.get_image_dims() got an unexpected keyword argument 'return_axes'

Spot Detection in Fourier Transform#

Change the spot_threshold value slightly up or down (down is more spots)

# ------Input----------
spot_threshold=0.15
# ---------------------

spots, _ = pyTEMlib.image_tools.diffractogram_spots(power_spectrum, spot_threshold=spot_threshold)
spots = spots[np.linalg.norm(spots[:,:2],axis=1)<7,:]
spots = spots[np.linalg.norm(spots[:,:2],axis=1)>0.5,:]
power_spectrum.plot()

plt.gca().scatter(spots[:,0],spots[:,1], color='red', alpha=0.4);

Found 19 reflections

dataset.provenance

{'sidpy': {'from_array_': '_0.12.7_2025-03-19-18:27:18.331601'}}