Object-oriented SPM files

A simple python package for working with Scanning Probe Microscopy (SPM) files, specifically for Bruker AFM files.

Setup

Requires Python >= 3.9 with numpy and pint for units, optionally matplotlib for plotting examples below.

Install the module from the GitHub url

pip install git+https://github.com/kogens/spmpy

Loading SPM files

An .spm file is represented by the SPMFile class and can be loaded by passing the path directly to SPMFile:

>>> from spmpy import SPMFile
>>> spm_file = SPMFile('path/to/afm_testfile.spm')
>>> print(spm_file)
SPM file: afm_testfile.spm, 2023-05-24 10:55:19. Images: ['ZSensor', 'AmplitudeError', 'Phase']

All the metadata is accessible in spm_file.header where it is organized in the sections starting with * in the file. You can also access the parameter directly by treating SPMFile as a dict, e.g.

>>> scan_size = spm_file['Scan Size']
>>> z_sensor_scaling = spm_file['Sens. ZsensSens']
>>> print(scan_size)
4500.0 nm
>>> print(z_sensor_scaling)
Sens. ZsensSens: 872.0382 nm/V

Each parameter in the metadata is interpreted into relevant datatypes (numbers, strings etc), and parameters with units are represented as a Quantity from the Pint library.

Images in the SPM file

An SPM file usually has more than one image and can be accessed with spm_file.images:

>>> spm_file.images
{'ZSensor': AFM image "Height Sensor" [nm], (128, 128) px = (4.5, 4.5) micrometer,
 'AmplitudeError': AFM image "Amplitude Error" [mV*nm/V], (128, 128) px = (4.5, 4.5) micrometer,
 'Phase': AFM image "Phase" [deg], (128, 128) px = (4.5, 4.5) micrometer}

These images have already been converted to physical units and can be plotted directly:

import matplotlib.pyplot as plt

height_im = spm_file.images['ZSensor']
plt.imshow(height_im)
plt.show()

This will show the image with pixels on x and y-axis. For imshow() you can set an extent to show the physical units. Either calculate the extent with image.px_size_x and image.px_size_y or use the built in image.extent value. The coordinates are also available as image.x, image.y or meshgrids in image.meshgrid.

# Plot the height image with units
im = plt.imshow(height_im, extent=height_im.extent)
plt.title(height_im.title)
plt.xlabel(height_im.x.units)
plt.ylabel(height_im.y.units)
cbar = plt.colorbar(im)
cbar.set_label(f'{height_im["Image Data"]} [{height_im.units}]')
plt.show()

The underlying data is stored as Pint Quantity objects which handles the units and support most of the same operations as a numpy ndarray, such as addition, multiplication etc. with other images although this will currently strip the metadata

min_value, max_value = height_im.min(), height_im.max()

# Set zero point at the lowest value
height_im_zeroed = height_im - min_value

# Normalize image to have values from 0 to 1
height_im_normalized = (height_im-min_value)/(max_value-min_value)

For some operations you need the pure Numpy ndarray, e.g. for many scikit-image functions, and this can be accessed using the .magnitude or .m attribute:

# Get underlying numpy ndarray
raw_numpy_array = height_im.m
print(type(raw_numpy_array))

All images found in the SPM file, such as phase and amplitude error images can be plotted like so

# Plot images in SPM file
fig, ax = plt.subplots(ncols=len(spm_file.images))
for i, image in enumerate(spm_file.images.values()):
    im = ax[i].imshow(image, extent=image.extent)
    ax[i].set_title(image.title)
    ax[i].set_xlabel(image.x.units)
    ax[i].set_ylabel(image.y.units)

plt.tight_layout()
plt.show()