Live radial profile Diff or FFT
Python - Gatan
Compute radial-max profiles of either the FFTs of HRTEM images or diffraction patterns over time. You can compute profiles from the 2D live-view image in DigitalMicrograph or in-situ 2D image data played back via the In-Situ Player. The result is a single 2D display of these profiles over time, with time as one dimension and radius as the other. Profiles are computed as often as possible.
Live radial profile
Python - Gatan
Code to compute radial-max profiles of the FFT of a live-view image in DigitalMicrograph, producing a 2D display of these profiles over time.
Live noise reduction
Python - Gatan
Code to process the live-view image in DigitalMicrograph, producing a new filtered display. A median and/or Gaussian blur can be applied.
Live max FFT
Python - Gatan
Code to process the live-view image in DigitalMicrograph, producing an FFT-like display. A grid of small FFTs is computed from the image, and then rather than averaging the FFTs together, the maximum across all FFTs is taken pixel-by-pixel.
Live FFT color map
Python - Gatan
Code to process the live-view image in DigitalMicrograph, producing a series of maps of crystalline regions in real-time. It computes a grid of small FFTs from each image. It then treats this grid similar to a 4D STEM data cube, where it finds the brightest spot in each FFT and displays its orientation and intensity as an updating color map.
Live difference
Python - Gatan
Code to process the live-view image in DigitalMicrograph, producing a difference image. Two exponentially weighted moving averages are computed, and the absolute value of the difference between them is returned.
IS dataset radial FFT profile
Python - Gatan
Compute radial-max profiles over time from the FFT of in-situ video datasets in DigitalMicrograph.
In-situ dataset radial profile Diff or FFT
Python - Gatan
Compute radial-max profiles from an in-situ dataset in DigitalMicrograph. The data can be HRTEM images (from which the FFT will be computed) or diffraction patterns over time. The primary result is a single 2D display of these profiles over time, with time as one dimension and radius as the other. The script also generates an automatically synced in-situ dataset of the 1D radial profiles.
In-situ 4D STEM maximum spot mapping
Python - Gatan
Produce a series of colorful maps of crystalline regions from an in-situ 4D STEM dataset open in DigitalMicrograph. This uses a simple algorithm that finds the location and intensity of the maximum pixel in each pattern. The resulting in-situ video of color maps automatically syncs with the original data in DigitalMicrograph. While the results look like orientation maps, the script does not perform proper orientation mapping.