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        "description": "AllanTools\n==========\n\n.. image:: https://badge.fury.io/py/AllanTools.svg\n    :target: https://badge.fury.io/py/AllanTools\n.. image:: https://travis-ci.org/aewallin/allantools.svg?branch=master\n    :target: https://travis-ci.org/aewallin/allantools\n.. image:: http://readthedocs.org/projects/allantools/badge/?version=latest\n    :target: http://allantools.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n.. image:: https://coveralls.io/repos/github/aewallin/allantools/badge.svg?branch=master \n    :target: https://coveralls.io/github/aewallin/allantools?branch=master \n.. image:: https://app.fossa.io/api/projects/git%2Bgithub.com%2Faewallin%2Fallantools.svg?type=shield\n    :target: https://app.fossa.io/projects/git%2Bgithub.com%2Faewallin%2Fallantools?ref=badge_shield\n\nA python library for calculating Allan deviation and related \ntime & frequency statistics. `LGPL v3+ license <https://www.gnu.org/licenses/lgpl.html>`_.\n\n* Development at https://github.com/aewallin/allantools\n* Installation package at https://pypi.python.org/pypi/AllanTools\n* Discussion group at https://groups.google.com/d/forum/allantools\n* Documentation available at https://allantools.readthedocs.org\n \n\nInput data should be evenly spaced observations of either fractional frequency,\nor phase in seconds. Deviations are calculated for given tau values in seconds.\n\n=====================================   ====================================================\nFunction                                Description\n=====================================   ====================================================\n``adev()``                              Allan deviation\n``oadev()``                             Overlapping Allan deviation\n``mdev()``                              Modified Allan deviation\n``tdev()``                              Time deviation\n``hdev()``                              Hadamard deviation\n``ohdev()``                             Overlapping Hadamard deviation\n``totdev()``                            Total deviation\n``mtotdev()``                           Modified total deviation\n``ttotdev()``                           Time total deviation\n``htotdev()``                           Hadamard total deviation\n``theo1()``                             Theo1 deviation\n``mtie()``                              Maximum Time Interval Error\n``tierms()``                            Time Interval Error RMS\n``gradev()``                            Gap resistant overlapping Allan deviation\n=====================================   ====================================================\n\nNoise generators for creating synthetic datasets are also included:\n\n* violet noise with f^2 PSD\n* white noise with f^0 PSD\n* pink noise with f^-1 PSD\n* Brownian or random walk noise with f^-2 PSD \n\nMore details on available statistics and noise generators : `full list of available functions <functions.html>`_  \n\nsee /tests for tests that compare allantools output to other \n(e.g. Stable32) programs. More test data, benchmarks, ipython notebooks, \nand comparisons to known-good algorithms are welcome!\n\nInstallation \n------------\n\n\nInstall from pypi::\n    \n    pip install allantools\n\nLatest version + examples, tests, test data, iPython notebooks : clone from github, then install ::  \n\n    python setup.py install\n\n(see `python setup.py --help install` for install options)\n\nThese commands should be run as root for system-wide installation, or \nyou can use the `--user` option to install for your account only. \nExact command names may vary depending on your OS / package manager / target python version.\n\nBasic usage \n-----------\n\nMinimal example, phase data\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nWe can call allantools with only one parameter - an array of phase data.\nThis is suitable for time-interval measurements at 1 Hz, for example\nfrom a time-interval-counter measuring the 1PPS output of two clocks.\n\n::\n\n    >>> import allantools\n    >>> x = allantools.noise.white(10000)        # Generate some phase data, in seconds.\n    >>> (taus, adevs, errors, ns) = allantools.oadev(x)\n\nwhen only one input parameter is given, phase data in seconds is assumed\nwhen no rate parameter is given, rate=1.0 is the default\nwhen no taus parameter is given, taus='octave' is the default\n\nFrequency data example\n~~~~~~~~~~~~~~~~~~~~~~\n\nNote that allantools assumes non-dimensional frequency data input.\nNormalization, by e.g. dividing all data points with the average \nfrequency, is left to the user.\n\n::\n\n    >>> import allantools\n    >>> import pylab as plt\n    >>> import numpy as np\n    >>> t = np.logspace(0, 3, 50)  # tau values from 1 to 1000\n    >>> y = allantools.noise.white(10000)  # Generate some frequency data\n    >>> r = 12.3  # sample rate in Hz of the input data\n    >>> (t2, ad, ade, adn) = allantools.oadev(y, rate=r, data_type=\"freq\", taus=t)  # Compute the overlapping ADEV\n    >>> fig = plt.loglog(t2, ad) # Plot the results\n    >>> # plt.show()\n\n\n*New in 2016.11* : simple top-level `API <api.html>`_, using dedicated classes for data handling and plotting.\n\n::\n\n    import allantools # https://github.com/aewallin/allantools/\n    import numpy as np\n\n    # Compute a deviation using the Dataset class\n    a = allantools.Dataset(data=np.random.rand(1000))\n    a.compute(\"mdev\")\n\n    # New in 2019.7 : write results to file\n    a.write_result(\"output.dat\")\n\n    # Plot it using the Plot class\n    b = allantools.Plot()\n    # New in 2019.7 : additional keyword arguments are passed to \n    # matplotlib.pyplot.plot()\n    b.plot(a, errorbars=True, grid=True)\n    # You can override defaults before \"show\" if needed\n    b.ax.set_xlabel(\"Tau (s)\")\n    b.show()\n\n\nJupyter notebooks with examples \n-------------------------------\n\nJupyter notebooks are interactive python scripts, embedded in a browser, \nallowing you to manipulate data and display plots like easily. 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