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AllanTools
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AllanTools

Allan deviation and related time & frequency statistics library in Python

by Anders Wallin

2019.9 (see all)License:LGPLv3+
pypi i AllanTools
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AllanTools

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A python library for calculating Allan deviation and related time & frequency statistics. LGPL v3+ license <https://www.gnu.org/licenses/lgpl.html>_.

Input data should be evenly spaced observations of either fractional frequency, or phase in seconds. Deviations are calculated for given tau values in seconds.

===================================== ==================================================== Function Description ===================================== ==================================================== adev() Allan deviation oadev() Overlapping Allan deviation mdev() Modified Allan deviation tdev() Time deviation hdev() Hadamard deviation ohdev() Overlapping Hadamard deviation totdev() Total deviation mtotdev() Modified total deviation ttotdev() Time total deviation htotdev() Hadamard total deviation theo1() Theo1 deviation mtie() Maximum Time Interval Error tierms() Time Interval Error RMS gradev() Gap resistant overlapping Allan deviation ===================================== ====================================================

Noise generators for creating synthetic datasets are also included:

  • violet noise with f^2 PSD
  • white noise with f^0 PSD
  • pink noise with f^-1 PSD
  • Brownian or random walk noise with f^-2 PSD

More details on available statistics and noise generators : full list of available functions <functions.html>_

see /tests for tests that compare allantools output to other (e.g. Stable32) programs. More test data, benchmarks, ipython notebooks, and comparisons to known-good algorithms are welcome!

Installation

Install from pypi::

pip install allantools

Latest version + examples, tests, test data, iPython notebooks : clone from github, then install ::

python setup.py install

(see python setup.py --help install for install options)

These commands should be run as root for system-wide installation, or you can use the --user option to install for your account only. Exact command names may vary depending on your OS / package manager / target python version.

Basic usage

Minimal example, phase data


We can call allantools with only one parameter - an array of phase data.
This is suitable for time-interval measurements at 1 Hz, for example
from a time-interval-counter measuring the 1PPS output of two clocks.

::

    >>> import allantools
    >>> x = allantools.noise.white(10000)        # Generate some phase data, in seconds.
    >>> (taus, adevs, errors, ns) = allantools.oadev(x)

when only one input parameter is given, phase data in seconds is assumed
when no rate parameter is given, rate=1.0 is the default
when no taus parameter is given, taus='octave' is the default

Frequency data example
~~~~~~~~~~~~~~~~~~~~~~

Note that allantools assumes non-dimensional frequency data input.
Normalization, by e.g. dividing all data points with the average 
frequency, is left to the user.

::

    >>> import allantools
    >>> import pylab as plt
    >>> import numpy as np
    >>> t = np.logspace(0, 3, 50)  # tau values from 1 to 1000
    >>> y = allantools.noise.white(10000)  # Generate some frequency data
    >>> r = 12.3  # sample rate in Hz of the input data
    >>> (t2, ad, ade, adn) = allantools.oadev(y, rate=r, data_type="freq", taus=t)  # Compute the overlapping ADEV
    >>> fig = plt.loglog(t2, ad) # Plot the results
    >>> # plt.show()


*New in 2016.11* : simple top-level `API <api.html>`_, using dedicated classes for data handling and plotting.

::

    import allantools # https://github.com/aewallin/allantools/
    import numpy as np

    # Compute a deviation using the Dataset class
    a = allantools.Dataset(data=np.random.rand(1000))
    a.compute("mdev")

    # New in 2019.7 : write results to file
    a.write_result("output.dat")

    # Plot it using the Plot class
    b = allantools.Plot()
    # New in 2019.7 : additional keyword arguments are passed to 
    # matplotlib.pyplot.plot()
    b.plot(a, errorbars=True, grid=True)
    # You can override defaults before "show" if needed
    b.ax.set_xlabel("Tau (s)")
    b.show()


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 /examples for some examples in notebook format.

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

* https://github.com/aewallin/allantools/blob/master/examples/noise-color-demo.ipynb
* https://github.com/aewallin/allantools/blob/master/examples/three-cornered-hat-demo.ipynb


Authors 
-------
* Anders E.E. Wallin, anders.e.e.wallin "at" gmail.com , https://github.com/aewallin
* Danny Price, https://github.com/telegraphic 
* Cantwell G. Carson, carsonc "at" gmail.com 
* Frédéric Meynadier, https://github.com/fmeynadier
* Yan Xie, https://github.com/yxie-git
* Erik Benkler, https://github.com/EBenkler

GitHub Stars

155

LAST COMMIT

10mos ago

MAINTAINERS

1

CONTRIBUTORS

23

OPEN ISSUES

20

OPEN PRs

1
VersionTagPublished
2019.9
3yrs ago
2019.7
3yrs ago
2019.7rc1
3yrs ago
2018.3
4yrs ago
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