Metadata-Version: 1.1
Name: SPIEPy
Version: 0.1.0
Summary: SPIEPy (Scanning Probe Image Enchanter using Python) is a Python library to improve automatic processing of SPM images.
Home-page: UNKNOWN
Author: Stephan Zevenhuizen
Author-email: S.J.M.Zevenhuizen@uu.nl
License: GPLv3
Description: 

        Python is a great language to use for automatic processing of scientific data.

        Scanning probe microscopes (SPM) produce scientific data in the form of images,

        images of surfaces that can have atomic or molecular resolutions. The microscope

        produces surfaces that are not level. Before you can analyse the surface, the

        surface must first be levelled (flattened). This Python library provides

        routines to flatten the surface and to generate statistical data on surface

        structures. Surfaces with contaminations, step edges and atomic or molecular

        resolution can be handled.

        

        The library SPIEPy has the packages spiepy with the modules for the tasks

        described above and spiepy.demo to generate sample data. With this sample data,

        you can familiarize yourself with SPIEPy.

        

        Dependencies

        ------------

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

        (http://scipy.org) and the Matplotlib library (http://matplotlib.org). You must

        install them manually.

        

        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, ...

        

        .. code-block:: pycon

        

        	>>> import spiepy, spiepy.demo

        	>>> help(spiepy)

        	...

        	>>> help(spiepy.demo)

        	...

        	>>> help(spiepy.flatten_by_iterate_mask)

        	...

        

        **Documentation:** http://pythonhosted.org/SPIEPy/ 

        	

        Example usage

        -------------

        .. code-block:: python

        

        	# -*- coding: utf-8 -*-

        	#

        	#   Copyright © 2014 Stephan Zevenhuizen

        	#   Flattening terrace image, (02-12-2014).

        	#

        

        	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 [#]_

        

        ..	[#] Condensed Matter and Interfaces, Debye Institute for Nanomaterials

        	Science, Utrecht University, Utrecht, The Netherlands.
Keywords: SPM scanning probe microscopy image analysis flattening nano nanotechnology
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
