Carter Francis

Within `pyxem` we could easily pull out almost the entirety of the utils folder https://github.com/pyxem/pyxem/tree/main/pyxem/utils and that would be pretty good start. We would have to reorganize some the functions...

It would be worth thinking about how to handle different array types/ meta-array types as well. For example: array types would be something like `numpy`, `cupy`, and `sparse` and meta-array's...

That sounds like a good first step. I might try to take a crack at this next week. I was intending to do some package organization before > So essentially...

> Looks interesting but very computationally intensive I would imagine. Not sure about the RAM issue you describe though. For 4., would it make sense to identify the coordinates that...

I'll write up a cross-correlation method tomorrow

@hakonanes and @din14970 thanks for the insight! > I know the template matching code (mostly Niels' [@din14970]), but there are no automatic benchmarks or measure of memory use as far...

> > the current template matching approach, just comparing vector positions. > > Not sure which one that would be ... Your implementation takes the position of the calculated diffraction...

This might not be helpful but this is now a working example: ```python import numpy as np import matplotlib.pyplot as plt from skimage.draw import ellipse from scipy.ndimage import correlate def...

@din14970 thanks for looking at this again! Your thoughts are very helpful! > > multiplies the r position of the diffraction spots by the strip in theta at that r...

Showing the profile a bit better..... ```python from scipy.ndimage import correlate def create_diffraction_strip(vector, radial_r, theta_r, theta_positions): rr,cc = ellipse(vector[0], vector[1],radial_r, theta_r, ) strip = np.zeros((radial_r*4, theta_positions)) strip[rr,cc]=1 return strip fig,...