Error while using correct_lines/ correct_slope on .spm- (Bruker-) data with different x-,y-dimension

[lukas271828]

Describe the bug A clear and concise description of what the bug is.

When spm data is loaded while using the Bruker and SPM module, the functions correct_lines and correct_slope do not work and return the following Error:

"self.pixels -= np.tile(np.mean(self.pixels, axis=1).T, (self.pixels.shape[0], 1)).T ValueError: operands could not be broadcast together with shapes (128,1024) (128,128) (128,1024)"

Furthermore, the plot with .show(pixels='False') returns an image with wrong y-dimension: here both axes are shown with the same range of the x axes.

The function works fine if i use an image with same x and y dimension.

To Reproduce Snippet of code creating the error:

filename = 'testfile.spm' scan = pySPM.Bruker(filename) topoB.correct_lines(inline=True) topoB.show()

topoB = scan.get_channel("Height Sensor")

Expected behavior A clear and concise description of what you expected to happen.

Screenshots If applicable, add screenshots to help explain your problem.

Information:

• OS: [e.g. windows]
  • Python and pySPM version.

Please run the following and attach the result to your issue

import sys
import pySPM
import numpy as np
import scipy
import matplotlib as mpl
print("Python",sys.version)
print("pySPM",pySPM.__version__)
print("numpy",np.__version__)
print("scipy",scipy.__version__)
print("matplotlib", mpl.__version__)

Python 3.9.13 (main, Aug 25 2022, 23:51:50) [MSC v.1916 64 bit (AMD64)] pySPM 0.2.23 numpy 1.21.5 scipy 1.9.1 matplotlib 3.5.2

Additional context Add any other context about the problem here.

[dineshpinto]

So this is a known limitation, where the correct_ functions only work on square arrays. I don't have some raw data to test with atm, but can you try this and see if it works correctly

import numpy as np
import pySPM

filename = 'testfile.spm'
scan = pySPM.Bruker(filename)
topoB = scan.get_channel("Height Sensor")

# the correct lines logic
topoB.pixels -= np.tile(np.mean(topoB.pixels, axis=1).T, (topoB.pixels.shape[1], 1)).T

# check if the result is correct
topoB.show(pixels=True)

[lukas271828]

Hello, your suggestion worked perfectly and solves the problem for me. Thank you for the solution and the qucik reply!