Image Tools

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')

installing pyTEMlib
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
import scipy

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, '../../')

%load_ext autoreload
%autoreload 2
import pyTEMlib


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'

pyTEMlib version:  0.2026.6.0

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(cmap='grey')
datasets.keys(), dataset.metadata['filename']

(dict_keys(['Channel_000']),
 'C:\\Users\\gduscher\\OneDrive - University of Tennessee\\2026 Experiments\\20260706-CsBrPb RCO\\A1\\0048 - 20260706 5900 x STEM 17 µm.emd')

dataset

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()

fft :
	smoothing : 1
	minimum_intensity : 10.122878251923055
	maximum_intensity : 17.86281328568153
source:  p1-3-hr3

Spot Detection in Fourier Transform¶

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

# ------Input----------
spot_threshold=0.05
# ---------------------

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);
print(f"found {len(spots)}")

found 22

Adaptive Fourier Filter¶

filtered_dataset = pyTEMlib.image_tools.adaptive_fourier_filter(image, spots, 
                                                                low_pass=2.0, reflection_radius=.3)
view = filtered_dataset.plot(cmap='gray')

Let’s see what we did - In Fourier space, of course.

filtered_power_spectrum = pyTEMlib.image_tools.power_spectrum(filtered_dataset, smoothing=0)

power_spectrum.view_metadata()
print('source: ', power_spectrum.source)
view = filtered_power_spectrum.plot()

fft :
	smoothing : 1
	minimum_intensity : 10.122878251923055
	maximum_intensity : 17.86281328568153
source:  p1-3-hr3

Please note that the spots are ordered from center to outside.

The third parameter of a spot is its angle.

print(spots[:5])

[[-2.62027028  2.21908057 -0.86811058]
 [ 2.62027028 -2.21908057  2.27348207]
 [-3.4226497   0.43880124 -1.44328677]
 [ 3.4226497  -0.43880124  1.69830589]
 [-3.22205484 -1.25371784 -1.94187537]]

Log the result¶

filtered_dataset.title = 'Fourier_Filtered_Image'
datasets.update({'Log_000': filtered_dataset})

Save Datasets to file¶

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

A convenient function to select a dataset (for further processing, visualization or whatever)

chooser = pyTEMlib.file_tools.ChooseDataset(datasets)

chooser.dataset.metadata

{'analysis': 'adaptive fourier filtered',
 'spots': array([[-2.62027028,  2.21908057, -0.86811058],
        [ 2.62027028, -2.21908057,  2.27348207],
        [-3.4226497 ,  0.43880124, -1.44328677],
        [ 3.4226497 , -0.43880124,  1.69830589],
        [-3.22205484, -1.25371784, -1.94187537],
        [ 3.22205484,  1.25371784,  1.19971728],
        [ 1.31640373, -3.23459202,  2.75508635],
        [-1.31640373,  3.23459202, -0.38650631],
        [-2.10624597, -2.80832796, -2.49809154],
        [ 2.10624597,  2.80832796,  0.64350111],
        [-3.51040995,  0.33850382, -1.47466498],
        [ 3.51040995, -0.33850382,  1.66692768],
        [-0.60178456, -3.48533559, -2.97061654],
        [ 0.60178456,  3.48533559,  0.17097612],
        [-2.21908057, -2.93369974, -2.49400159],
        [ 2.21908057,  2.93369974,  0.64759107],
        [-4.73905343,  3.67339327, -0.91140362],
        [ 4.73905343, -3.67339327,  2.23018903],
        [-5.52889567, -2.36952671, -1.97568811],
        [ 5.52889567,  2.36952671,  1.16590454],
        [-0.78984224, -6.0303828 , -3.01135688],
        [ 0.78984224,  6.0303828 ,  0.13023578]]),
 'low_pass': 2.0,
 'reflection_radius': 0.3}

A tree-like plot of the file

print(dataset.h5_dataset.file.filename)
pyTEMlib.file_tools.h5_tree(dataset.h5_dataset.file)

C:\Users\gduscher\Documents\Github\pyTEMlib\example_data\p1-3-hr3-1.hf5
/
├ Measurement_000
  ---------------
  ├ Channel_000
    -----------
    ├ p1_3_hr3
      --------
      ├ metadata
        --------
        ├ experiment
          ----------
      ├ original_metadata
        -----------------
        ├ DM
          --
        ├ DocumentObjectList
          ------------------
          ├ 0
            -
            ├ AnnotationGroupList
              -------------------
              ├ 0
                -
                ├ Font
                  ----
                ├ ObjectTags
                  ----------
            ├ ImageDisplayInfo
              ----------------
              ├ DimensionLabels
                ---------------
              ├ MainSliceId
                -----------
            ├ ObjectTags
              ----------
        ├ DocumentTags
          ------------
        ├ Image Behavior
          --------------
          ├ UnscaledTransform
            -----------------
          ├ ZoomAndMoveTransform
            --------------------
        ├ ImageData
          ---------
          ├ Calibrations
            ------------
            ├ Brightness
              ----------
            ├ Dimension
              ---------
              ├ 0
                -
              ├ 1
                -
          ├ Dimensions
            ----------
        ├ ImageSourceList
          ---------------
          ├ 0
            -
            ├ Id
              --
        ├ ImageTags
          ---------
          ├ Acquisition
            -----------
            ├ Device
              ------
              ├ CCD
                ---
              ├ Configuration
                -------------
                ├ Transpose
                  ---------
            ├ Frame
              -----
              ├ Area
                ----
                ├ Transform
                  ---------
                  ├ Transform List
                    --------------
                    ├ 0
                      -
                      ├ Transpose
                        ---------
              ├ CCD
                ---
              ├ Intensity
                ---------
                ├ Range
                  -----
                ├ Transform
                  ---------
                  ├ Transform List
                    --------------
                    ├ 0
                      -
                    ├ 1
                      -
              ├ Reference Images
                ----------------
                ├ Dark
                  ----
              ├ Sequence
                --------
            ├ Parameters
              ----------
              ├ Base Detector
                -------------
              ├ Detector
                --------
              ├ Environment
                -----------
              ├ High Level
                ----------
                ├ Shutter
                  -------
                ├ Transform
                  ---------
              ├ Objects
                -------
                ├ 0
                  -
                ├ 1
                  -
                ├ 2
                  -
                ├ 3
                  -
          ├ DataBar
            -------
            ├ Custom elements
              ---------------
          ├ Microscope Info
            ---------------
            ├ Items
              -----
              ├ 0
                -
              ├ 1
                -
              ├ 2
                -
        ├ MinVersionList
          --------------
          ├ 0
            -
        ├ Page Behavior
          -------------
          ├ PageTransform
            -------------
        ├ SentinelList
          ------------
        ├ Thumbnails
          ----------
          ├ 0
            -
        ├ UniqueID
          --------
      ├ p1_3_hr3
      ├ x
      ├ y
  ├ Log_000
    -------
    ├ Fourier_Filtered_Image
      ----------------------
      ├ Fourier_Filtered_Image
      ├ metadata
        --------
      ├ x
      ├ y

Close File¶

let’s close the file but keep the filename

filename = dataset.h5_dataset.file.filename
dataset.h5_dataset.file.close()

Simulate new notebook¶

We can now simulate a new notebook and open the file again.

new_dataset= pyTEMlib.file_tools.open_file(filename)
choose_image = pyTEMlib.file_tools.ChooseDataset(new_dataset)

view = choose_image.dataset.plot()

We want to make an image operation of the images in the file.

choose_image = pyTEMlib.file_tools.ChooseDataset(new_dataset)
print('       subtract')
choose_image2 = pyTEMlib.file_tools.ChooseDataset(new_dataset)

       subtract

new_image = np.array(choose_image.dataset) - np.array(choose_image2.dataset)
new_image = choose_image.dataset.like_data(new_image)
vew = new_image.plot()

choose_image.dataset.metadata

{'filename': 'C:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\example_data\\p1-3-hr3.dm3',
 'experiment': {'acceleration_voltage': 199990.28125,
  'exposure_time': 1.0,
  'microscope': 'Libra 200 MC'}}