tweezepy 1.1.1

Single-molecule force spectroscopy calibration

Tweezepy

This is Tweezepy, a Python package for calibrating forces in single-molecule force spectroscopy video-tracking experiments using the power spectral density (PSD) and Allan variance (AV).

Documentation

Read the documentation for Tweezepy here.

How to install

The simplest method of installing the Tweezepy package is via the Python Package Index (PyPI). To install from PyPI, you will need to be able to run python from the command line and make sure you have pip available.

Install from PyPI:

pip install tweezepy

An alternative method to install Tweezepy is with setuptools. Clone the repository onto a local machine, then navigate to the directory.

Using setuptools:

cd path/to/tweezepy

python setup.py install

Contents

The Tweezepy package includes the following modules:

• 'smmcalibration' - classes for calibration methods using the PSD and AV
• 'expressions' - functions with closed-form expressions for thermal motion in the PSD and AV
• 'MLE' - classes for maximum likelihood estimation (MLE) and Monte Carlo Markov chain (MCMC) sampling
• 'allanvar' - tools for calculating the AV and equivalent degrees of freedom
• 'simulations' - tools to simulate bead thermal motion

Example use:

Simulate data:

>>> import matplotlib.pyplot as plt
>>> from tweezepy.simulations import downsampled_trace
>>> xtrace = downsampled_trace()
>>> plt.plot(xtrace)
>>> plt.show()

Power spectral density:

>>> from tweezepy.smmcalibration import PSD
>>> psd = PSD(xtrace,fsample)
>>> psd.mlefit()
>>> print(psd.results)
>>> psd.plot()

Allan variance:

>>> from tweezepy.smmcalibration import AV
>>> av = AV(xtrace,fsample)
>>> av.mlefit()
>>> print(av.results)
>>> av.plot()

Jupyter notebooks with examples

Jupyter notebooks are interactive Python scripts, embedded in a browser, allowing you to manipulate data and display plots like easily. For guidance on installing jupyter, please refer to https://jupyter.org/install.

See /docs for some examples in notebook format.

github formats the notebooks into nice web-pages, for example

• Basic usage
• MCMC