{
    "info": {
        "author": "Timo Lesterhuis",
        "author_email": "timolesterhuis@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
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        "description": "..  |logo| image:: https://raw.githubusercontent.com/tim00w/diagnostics/master/docs/images/analysis.svg?sanitize=true\n   :scale: 10%\n\nDiagnostics - A toolbox for analyzing diagnostic data\n=====================================================\n\n.. image:: https://travis-ci.org/tim00w/diagnostics.svg?branch=master\n   :target: https://travis-ci.org/tim00w/diagnostics\n   :alt: Build Status\n\n.. image:: https://coveralls.io/repos/github/tim00w/diagnostics/badge.svg?branch=master\n   :target: https://coveralls.io/github/tim00w/diagnostics?branch=master\n   :alt: Coverage Status\n\n.. image:: https://readthedocs.org/projects/diagnostics/badge/?version=latest\n   :target: https://diagnostics.readthedocs.io/en/latest/?badge=latest\n   :alt: Documentation Status\n\n.. image:: https://mybinder.org/badge_logo.svg\n   :target: https://mybinder.org/v2/gh/tim00w/diagnostics/master?filepath=examples/example.ipynb\n   :alt: Binder\n\n.. image:: https://img.shields.io/pypi/v/pydiagnostics.svg?color=blue\n   :target: https://pypi.org/project/pydiagnostics/\n   :alt: PyPi version\n\n.. image::  https://img.shields.io/pypi/l/pydiagnostics.svg?color=purple\n   :target: https://github.com/tim00w/diagnostics/blob/master/LICENSE\n   :alt: License: MIT\n\n.. image:: https://img.shields.io/badge/code%20style-black-000000.svg\n   :target: https://github.com/ambv/black\n   :alt: Code style: black\n\n|\n\n**Diagnostics** is a Python module designed to make analysis of diagnostic data easier.\nIt comes with a couple of clear data-structures with automatic quality checks, easy\nBoolean logic operators and built-in bookkeeping. To top that off, it's built on `numpy! <https://www.numpy.org>`_\n\nInstallation\n------------\n\nThe diagnostics library is tested on python 3.7. However, it should run\non python 3.6 and 3.5 as well.\n\nYou can install the library using ``pip``:\n\n.. code:: bash\n\n    pip install pydiagnostics\n\nAlternatively, you can clone the repository and use ``setup.py`` to\ninstall:\n\n.. code:: bash\n\n    git clone https://github.com/tim00w/diagnostics.git\n    cd diagnostics\n    python setup.py install\n\nOpen Source\n-----------\n\nThis project strives to abide by generally accepted best practices in open-source software development:\n\n.. image:: https://bestpractices.coreinfrastructure.org/projects/2796/badge\n   :target: https://bestpractices.coreinfrastructure.org/projects/2796\n   :alt: CII Best Practices\n\nDocumentation\n-------------\n\nThe docs are hosted on `ReadTheDocs <https://diagnostics.readthedocs.io/en/latest/>`_.\n\n\nQuickstart\n----------\n\nEager to begin and no time to read the documentation? Here is a quickstart!\n\nTimeSeries\n^^^^^^^^^^\n\nDiagnostic events are derived from from real occurances. For instance,\nyour phone will probably generate a message (event) if your battery is\nrunning low (percentage below threshold value). The diagnostics library\nhas a ``TimeSerie`` class that can capture these occurances.\n\nFor example, a ``TimeSerie`` representing your battery life, which\ndrains 0.01% each second:\n\n.. code:: python\n\n    import numpy as np\n    import diagnostics as ds\n\n    battery_life = ds.TimeSerie(np.arange(100, 0, -0.01), fs=1)\n\nthe first argument is consists of a data array (both ``list()`` and\n``numpy.array()`` are supported), and additionally you can provide some\nkeyword parameters. Here we've provided the sample frequency (``fs``)\nwhich is 1 Hz, because we said our battery drains 0.01% each second. In\nthis particular case we could've left ``fs`` out, since the default\nvalue of ``fs`` is also 1.\n\nNow that we've got our data, we can easily visualize this:\n\n.. code:: python\n\n    battery_life.plot(show=True)\n\nThere are other keyword parameters that we can use as well, such as t0\n(start time of ``TimeSerie`` in posixtime or a ``datetime`` object), and\na name (default is an empty string).\n\n.. code:: python\n\n    from datetime import datetime\n\n    battery_life = ds.TimeSerie(np.arange(100, 0, -0.01),\n                                fs=1,\n                                t0=datetime(2019,1,1,8,5), # 2019-01-01 08:05\n                                name='battery life')\n\nNow we've got our battery life set to a specific (start-)datetime, and gave it a\nname. Both will come in handy later.\n\nBooleanTimeSeries\n^^^^^^^^^^^^^^^^^\n\nLet's be honest, the battery percentage of your phone does not really\nmatter to you, unless it goes below a certain threshold. Luckily for us,\nour ``TimeSerie`` can easily be converted to a ``BooleanTimeSerie``,\nwhich only contains boolean values of when the percentage reaches below\n25%:\n\n.. code:: python\n\n    battery_below25 = battery_life <= 25\n\n    battery_below25.plot(show=True)\n\nNow that's easy! We can see that our battery goes below 25% at HH:MM:SS.\n\nStateChangeArray\n^^^^^^^^^^^^^^^^\n\nYou could argue that our ``BooleanTimeSerie`` contains a lot of data\npoints with the same value. I'd agree with you, and therefore introduce\na class that only keeps track of the changes in data points, the\n``StateChangeArray``:\n\n.. code:: python\n\n    battery_low_state = battery_below25.to_statechangearray()\n\nAlternatively, we can create a ``StateChangeArray`` (or\n``BooleanStateChangeArray``, you can probably guess the difference\n:smile:) from scratch:\n\n.. code:: python\n\n\n    s = ds.StateChangeArray([1, 4, 8, 13], t=[1,2,4,8], name='my state')\n    b = ds.BooleanStateChangeArray([True, False, True, False], t=[1,3,6,9], name='b')\n\nBoth the data array as the values for time (``t``) can be ``list()`` or\n``np.array()``. The time is considered as posixtime. For now it is not\npossible to give a datetimearray or list of datetimes as an input, but\nthis wil be implemented in the near future.\n\nComparing TimeSeries and StateChangeArrays\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nThere are more classes besides TimeSeries and StateChangearrays, each\nwith their own advantages and disadvantages. The power of this module\nlies in clear transformations from one class to another (we've already\nshown the ``TimeSerie.to_statechangearray()`` method), and the\ncomparison of multiple classes.\n\nTo start with TimeSeries, if two (or more) have the same array\\_length,\n``t0`` and ``fs``, we can easily do calculations with them!\n\n.. code:: python\n\n    # create two TimeSerie objects that we'll combine\n    a = ds.TimeSerie(np.sin(np.linspace(0, 2*np.pi, 100)), t0=0, fs=1, name='a')\n    b = ds.TimeSerie(np.sin(2* np.linspace(0, 2*np.pi, 100)), t0=0, fs=1, name='b')\n\n    # It's this easy!\n    c = a + b\n\n    # We're interested in the more extreme values, lets create TimeSeries for these:\n    d = c <= -1\n    e = c >=  1\n\n    # we'll name them to keep our bookkeeping up to date\n    d.name = 'c <= -1'\n    e.name = 'c >= 1'\n\n    # and find when one of the above conditions is True!\n    f = d | e\n\n    # when performing boolean operators ('~', '^', '&', '|'), the library\n    # does it's own bookkeeping:\n    print(f.name)\n    f.plot(show=True)\n\nComparing StateChangeArrays would normally be a bit tricky, since the\ndata is most likely non-linearly spaced. This means that we can't just\nperform vectorized boolean operations, but we'll need to combine both\ndata values as well as their respective points in time.\n\nLuckily for us, the ``StateChangeArray`` has this built in:\n\n.. code:: python\n\n    a = StateChangeArray([True, False, True, False], t=[2,4,6,8], name='a')\n    b = StateChangeArray([True, False, True, False], t=[3,5,7,9], name='b')\n\n    c = a | b\n    d = a & b\n    e = ~a\n    f = a ^ a\n    g = a ^ e\n\nThat's pretty great right?\n\nReports & Events\n^^^^^^^^^^^^^^^^\n\nWIP\n\n.. image:: https://img.shields.io/pypi/dm/pydiagnostics.svg\n   :target: https://pypi.org/project/pydiagnostics/\n   :alt: Downloads\n\n\n\n",
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