Installation

Install GloPel via pip:

$ pip install glopel

Run GloPel

Run GloPel on the console via:

$ GloPel

Alternatively, call it as package when running Python:

>>> from GloPel.GloPel import run
>>> run()

Various subfunctions of GloPel can be used modulary.

Generate a discrete 5x5 second derivative operator:

>>> from GloPel.Regularization import second_order_diffential_operator
>>> diffop = second_order_diffential_operator(5)
>>> print(diffop)
[[-2.  1.  0.  0.  0.]
 [ 1. -2.  1.  0.  0.]
 [ 0.  1. -2.  1.  0.]
 [ 0.  0.  1. -2.  1.]
 [ 0.  0.  0.  1. -2.]]

Generate a PELDOR kernel matrix for t = 0 to t = 2000 ns, using 131 points in the distance domain:

>>> import numpy as np
>>> from GloPel.Regularization import kernel_matrix
>>> time = np.linspace(0, 2000, 500)
>>> points = 131
>>> kernel = kernel_matrix(time, points)

Properties

GloPel provides:

• PELDOR/DEER data processing

• Tikhonov regularization

• Model-based fitting

• Global analysis (Tikhonov and fitting) of two time traces

• Validation tools

• User friendly graphical user interface

• Automatically generated reports

Feedback

We are eager to hear about your experiences with GloPel. You can email me at stephan.rein@physchem.uni-freiburg.de.

Acknowledgement

A number of people have helped shaping GloPel and the ideas behind. First and foremost, Prof. Dr. Stefan Weber and Dr. Sylwia Kacprzak (now Bruker Biospin) were for years the driving force behind GloPel due to the need for advanced analysis of PELDOR/DEER data with limited signal-to-noise ratio. Dr. Till Biskup (now Saarland University) contributed ideas for programming and details of the implementation that make all the difference between a program useful for a larger audience and a simple in-house solution.

Bugfixes

31.08.2020 (Error message appearing for global validation without additive noise)