Dependencies

SPIEPy requires the NumPy library (http://www.numpy.org), SciPy library (http://scipy.org) and the Matplotlib library (http://matplotlib.org).

Installation

Using pip:

> pip install SPIEPy

CLASSES

Im

SPIEPy_image_structure, set attribute data with a 2D ndarray of image data, set all other attributes with the metadata of the image.

FUNCTIONS

Flattening functions:

• flatten_by_iterate_mask

• flatten_by_peaks

• flatten_poly_xy

• flatten_xy

Locating functions:

• locate_masked_points_and_remove

• locate_regions

• locate_steps

• locate_troughs_and_peaks

Masking functions:

• mask_by_mean

• mask_by_troughs_and_peaks

• mask_tidy

Measuring functions:

• measure_feature_properties

Demo functions:

• list_demo_files

• load_demo_file

DATA

NANOMAP

Colormap which is the standard orange colormap used my most SPM software.

Help

On the interpreter console use the built-in help function to get the help page of the module, function, …

>>> import spiepy, spiepy.demo
>>> help(spiepy)
...
>>> help(spiepy.demo)
...
>>> help(spiepy.flatten_by_iterate_mask)
...

Documentation: https://webspace.science.uu.nl/~zeven101/SPIEPy/

Example usage

# -*- coding: utf-8 -*-
#
#   Copyright © 2014 - 2017 Stephan Zevenhuizen
#   Flattening terrace image, (09-10-2017).
#

import spiepy, spiepy.demo
import matplotlib.pyplot as plt
import numpy as np

im = spiepy.Im()
demos = spiepy.demo.list_demo_files()
print(demos)
im.data = spiepy.demo.load_demo_file(demos[1])

plt.imshow(im.data, cmap = spiepy.NANOMAP, origin = 'lower')
print('Original image.')
plt.show()

im_out, _ = spiepy.flatten_xy(im)
plt.imshow(im_out.data, cmap = spiepy.NANOMAP, origin = 'lower')
print('Preflattened image.')
plt.show()

mask = spiepy.locate_steps(im_out, 4)
plot_image = np.ma.array(im_out.data, mask = mask)
palette = spiepy.NANOMAP
palette.set_bad('#00ff00', 1.0)
plt.imshow(plot_image, cmap = palette, origin = 'lower')
print('Preflattened image, mask.')
plt.show()

im_final, _ = spiepy.flatten_xy(im, mask)
plt.imshow(im_final.data, cmap = spiepy.NANOMAP, origin = 'lower')
print('Flattened image.')
plt.show()

y, x = np.histogram(im_out.data, bins = 200)
ys, xs = np.histogram(im_final.data, bins = 200)
fig, ax = plt.subplots()
ax.plot(x[:-1], y, '-b', label = 'Standard plane flattening')
ax.plot(xs[:-1], ys, '-r', label = 'SPIEPy stepped plane flattening')
ax.legend(loc = 2, fancybox = True, framealpha = 0.2)
ax.set_xlabel('z (nm)')
ax.set_ylabel('count')
plt.show()

Authors & affiliations

Stephan J. M. Zevenhuizen [1]